For Data Scientists & ML Engineers · Fertilizers Agricultural Chemicals
Fertilizers & Ag Chemicals Data for Data Scientists
Fertilizers Agricultural Chemicals data for data scientists: 15 datasets on one shelf. Every one delivered as API, files, or warehouse rows.
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API, files, or your warehouse. Daily, weekly, or hourly.
How were these fertilizer datasets ranked for data scientists?
Every position comes from the Datadory quality rubric, a 0–10 score recorded per dataset on field documentation, access reliability and freshness. Across these 15 qualifying datasets the scores run from 6 to 9, averaging 7.93 — above the catalog-wide mean of 7.81 across the 1,744 datasets Datadory catalogs — and 11 of the 15 score 8 or higher.
How fresh are these fertilizer feeds?
Twelve of the 15 refresh annually, two are static snapshots (the Rwanda plot-level set and the CEH 2010–2015 raster surfaces) and only IndexMundi moves faster than yearly, at monthly. Plan around the slowest grain you join on: FAOSTAT panels land once per year, Defra's Farm Business Survey series publish per financial year (2015/16–2024/25), and Statistics Canada closes each fertilizer year before releasing the province panel.
Catalog-wide context: 22.6% of the 1,744 datasets Datadory catalogs.8% at least weekly — none of the 15 here reaches that cadence, so schedule incremental pulls rather than streaming.
What formats should your pipeline expect?
Ten of the 15 datasets deliver CSV in some form, so ingestion is rarely the bottleneck. The exceptions are instructive: Hugging Face's Rwanda set is Parquet-only (72 columns, model-ready), Defra's British Survey of Fertiliser Practice publishes ODS/ODT rather than flat CSV, USDA ERS concentrates everything in a single 32-table XLS workbook, CEH distributes 2-band TIFF rasters with supporting ZIP documentation, and IndexMundi offers HTML tables only.
Both Eurostat cubes speak SDMX and JSON-stat alongside CSV, which means you can validate structure definitions programmatically instead of eyeballing column headers — useful when a country-x-nutrient-x-year panel spans 211 geo codes.
How do you combine these sources in one pipeline?
Strongest feature sets join across sources: pair FAOSTAT's 245-country production/use rows with the World Bank's kilogram-per-hectare indicator for cross-country labels; combine Eurostat's consumption cube with its gross nutrient balance (1985–2023 components: mineral and organic fertiliser, manure, fixation, deposition) to engineer surplus targets; and align NASS percent-of-acres-treated panels with ERS application-rate tables for US crop-level features. Statistics Canada's province-x-nutrient panel extends the same schema northward from 2006/2007.
Browse the fertilizers-agricultural-chemicals data hub for the full industry slice, or check all data-scientists resources to compare this stack against other industries.
Straight answers
Which fertilizer time series can I pull through an API for backtesting?
Eurostat's aei_fm_usefert and gross nutrient balance cubes return JSON-stat, TSV, SDMX and CSV (annual, from 1995 and 1985 respectively), and the World Bank serves AG.CON.FERT.ZS — kg per hectare of arable land, 1961–2023 — as JSON, XML or CSV.
Where can I find alternative data for quantitative fertilizer research?
Hugging Face's Rwanda Season C set adds plot-level microdata — 2,307 rows x 72 Parquet columns — and Defra's Farm Business Survey splits application rates by farm type and economic output band.
Where do I get training data for fertilizer demand models?
Join FAOSTAT's 245-country production/trade/use panel with the World Bank's kilogram-per-hectare indicator for cross-country labels, add Eurostat's 1985–2023 gross nutrient balance as engineered surplus features, and hold out the Rwanda Parquet set for tabular-model evaluation.
Rows before rollout
Sample rows from any shelf entry — the field dictionary and coverage notes ride along. If the shelf misses what you need, say so; sourcing requests are half our job.
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