Crop yield by country: which record answers your question, delivered as rows

Yield is a derived ratio - production divided by the area that produced it - which is why every serious yield question starts with a second question: whose production, over whose area, measured how? Country-level yield data answers it at the national grain, and the shelf records the ratio in three different ways.

World Bank – Agriculture & Rural Development Indicators publishes yield pre-computed: one cereal yield figure in kilograms per hectare per economy per year, sitting inside a 49-series development panel scored 9 out of 10 on Datadory's rubric. USDA FAS PSD Online treats yield as one attribute of the full balance sheet - beside area harvested, production, trade, stocks and consumption - for 63 commodities across 214 countries, around 2.09 million observations in total. Our World in Data - Crop Yields curates the ratio into roughly 27 single-crop series from almond to wheat plus yield-gap analyses, each on one four-column skeleton.

Underneath all three sits FAOSTAT - Crops and Livestock Products, the production account where area harvested, production and yield arrive as three separate elements per commodity-year, with a provenance flag riding on every value. That is the layer to know about, because it is where the other three largely come from.

Two economies, two records, sixty-three years apart - exactly as the rows ship after normalization:

# world bank agriculture panel - cereal yield, kilograms per hectare
indicator : AG.YLD.CREL.KG   Cereal yield (kg per hectare)
country   : USA   United States
date      : 1970     value: 3154.9
date      : 2023     value: 8330
date      : 2024     value: 8413.5

# our world in data crop yields - wheat, tonnes per hectare
Entity       Code  Year  Wheat yield (tonnes per hectare)
Afghanistan  AFG   1961  1.0220001
Zimbabwe     ZWE   2024  4.8629003

Read them side by side and the two things that break most yield projects surface immediately. First, units: the development panel counts grams of cereal per square metre of cropland and calls them kilograms per hectare, while the crop collection reports the same physical quantity as tonnes per hectare - a factor of 1,000 that no join will catch for you if the unit column gets dropped upstream. Second, identity: both records key entities to stable codes rather than name strings, which is what lets a 1961 Afghanistan row and a 2024 Zimbabwe row load into one table without a reconciliation pass.

In the balance-sheet record the yield line looks different again: one row per commodity, country, market year and attribute, where Attribute_Description selects Yield out of the dozen-plus balance lines and Unit_Description carries the per-hectare basis beside the amount. Request a sample and you get rows shaped exactly like these, cut to the crops, countries and years you name.

Three structures divide the shelf, and each buys you something the other two cannot.

The pre-computed indicator is the fastest route to a clean country-year panel. The World Bank's cereal yield series ships as roughly 17,490 observations, one per economy per year, already harmonized - no filtering, no derivation, straight into a regression. The price is resolution: cereals as a basket, no wheat-versus-corn split, and nothing below the national line anywhere in the topic.

The commodity-first balance runs the other way. PSD Online's 2.09 million rows make you isolate yield by filtering the attribute column for your chosen commodities - wheat, corn, rice, soybeans, whatever the desk needs - and reward you with crop-level yield beside the stocks and consumption lines that explain why the yield moved. Its grain is the market year, which starts in a different month for grains than for coffee or cotton, so period handling matters more than it does elsewhere.

The chart-sized collection sits between: per-crop tables of roughly 3,000 to 10,000 rows on one Entity-Code-Year-measure skeleton, small enough to hold in memory whole, presentation-ready by construction, and drawn principally from the same FAO-compiled accounts as the other two.

And when none of the three cuts deep enough, the production account underneath them adds the element the ratios throw away: FAOSTAT - Crops and Livestock Products keeps area harvested and production beside yield for 312 items across 244 areas, so a yield collapse can be decomposed into numerator and denominator instead of stared at.

Four traps account for most wrong country rankings in circulation, and all four are visible in the rows themselves.

1. Aggregates sit beside countries. World, regional and income groupings ride the same geography column as sovereign states - in the crop collection they are the rows with a blank ISO alpha-3 code. Rank without filtering them out and the world total quietly tops your league table.

1. Empty means not-reported, not zero. A missing economy-year is a gap in measurement, frequently clustered among small economies and older decades. Impute zeros and every regional average you compute afterwards inherits the fiction.

1. Zeros and absences mean different things in the balances. In PSD Online a published 0.0000 is a real estimate of nil; a missing cell means no estimate exists. Balance math that treats them alike produces stocks-to-use ratios that are confidently wrong.

1. Provenance is graded per observation. In the production account every value carries a flag: official country figure, estimate, imputation or external contribution. Filter on official figures when a deliverable needs them, treat imputed stretches as modelled input rather than history, and no reviewer can take the ranking apart.

Add one habit and the rankings hold up in public: state the reference year beside every figure you quote, because these series are revised historically and a country can shift meaningfully on revision alone.

1961 is the honest floor nearly everywhere, and it is deep enough to matter: six decades of annual observations span the Green Revolution, its plateaus and its laggards on one axis. The World Bank agriculture series generally begin there, the crop-yield collection runs 1961 through 2024 with several long-run academic series reaching earlier for some countries, and the production account covers every year from 1961 through the 2024 reference build. The balance sheet runs longest of the global three, opening at market year 1960 and stretching through the current forecast.

Inside the United States the ceiling disappears. NASS Quick Stats holds survey estimate records reaching toward the mid-1800s - scored 10 out of 10 on Datadory's rubric, one of only 145 of 1,744 cataloged datasets to reach it - and the USDA Census of Agriculture has counted American farms every five years since 1840, with county-resolved tables throughout.

Mixing depths is where models quietly corrupt themselves. A finalized 2024 observation, a partial newest year and a current-year forecast that later revisions will replace are three different publication states wearing the same YYYY clothing. Timestamp every series you store, keep the vintage label beside each extract, and never let a forecast row masquerade as history in a trend chart.

A yield table alone invites overreading; four companions turn it into analysis.

Inputs, from the same panel. The World Bank record already carries fertilizer consumption per hectare of arable land, irrigated land share, machinery and tractors beside its yield series, so the yield-versus-fertilizer question needs no second source - and the collection's own analytical charts plot exactly that pairing across economies.

Trade flows, when yield crosses a border. FAOSTAT - Detailed Trade Matrix holds 52,410,630 bilateral rows of quantities and values for 572 items across 232 reporters from 1986 to 2024, turning a domestic productivity story into an import-dependence story on one join.

Prices, for the revenue side. IndexMundi - Agricultural Commodity Prices tracks roughly 90 monthly benchmark series back to July 1991, which converts a physical yield curve into a value curve at the same monthly grain.

Ground truth below the national line. USDA NASS Quick Stats resolves American yields from national totals down to counties and ZIP codes, so any global panel can be sanity-checked against the one market where sub-national measurement is deepest.

For the money behind the harvest, USDA ERS Farm Income and Wealth Statistics adds roughly 460,000 rows of net farm income and cash receipts reaching back to 1910.

Investors and quant researchers read yield trajectories as the supply-side spine of soft-commodity theses - long-run productivity per hectare beside the stock and consumption balances that price desks actually trade on.

Ag lenders and crop insurers anchor underwriting assumptions on multi-decade yield stability per country, then stress-test the assumption against the county-level American record where the resolution supports it.

Food and agribusiness strategists size sourcing regions and watch productivity shifts move the competitive map - a country whose yield growth outruns its rivals is a supplier worth qualifying early.

Data scientists and ML engineers get rare training material: a fixed-schema, sixty-plus-year country x crop x year panel with provenance flags intact, no schema drift between vintages to debug.

Journalists, academics and students get the citation-grade story - Green Revolution winners, plateau cases, yield gaps between neighbors - on definitions stable enough to quote across a decade of reporting.

All five jobs run off the same typed rows, keyed by entity, code and year rather than as charts you have to reverse-engineer.

Because the hard part was never finding a yield number - it is getting forty of them to agree. Kilograms per hectare against tonnes per hectare. Market years against calendar years. Official figures beside imputations that look identical. World totals squatting in the country column. Each shelf record solves one of these and leaves the rest for you, in a different format, on a different calendar.

Datadory handles that upstream of you: units travel on every row so per-hectare bases never mix silently, aggregates arrive pre-tagged so one predicate removes them, provenance flags survive into your warehouse instead of dying in someone's parser, and finalized observations stay separated from partial years and revisable forecasts. Delivered daily, weekly, or hourly - your call - into your warehouse, your notebook or your dashboard.

Start with a sample: name the crops, countries and date range, and the extract comes back shaped like your question, field dictionary attached.

Start with the agricultural products services data guide, which maps all 23 pooled records for the industry and shows where the yield sources sit beside trade, price and farm-finance sets. Then go deeper on the neighboring workflows: usda psd online data for the balance sheet behind every yield regime, agricultural commodity monthly prices for the revenue side, grape production by country for a worked single-commodity example, and where can i get free usda crop production data for the American records in depth.

Product pages with complete field dictionaries and verified samples: USDA FAS PSD Online, World Bank – Agriculture & Rural Development Indicators, Our World in Data - Crop Yields and FAOSTAT - Crops and Livestock Products. The agricultural products services data hub indexes every record, and best agricultural products services datasets puts the scorecard in one view. When you're ready to build, request a sample cut to the crops and years on your desk this quarter.