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Sown areas of raw cotton in Russia: breakdown by category of farms

· snapshot · rows: 6 · periods: 7 · section: Bulletins on the state of agriculture (electronic versions)

Raw cotton occupies a specific niche in Russian crop production, and these figures reveal the extent of its sown areas by farm category. Data is grouped by territory, including figures for the Russian Federation, the North Caucasian Federal District, and the Stavropol Krai. The table contains 2022 data, supplemented by calculations of dynamics relative to the previous period, allowing for a clear view of crop distribution between agricultural organizations and peasant farms

Table of raw cotton sown areas by farm category in 2022 as a percentage of 2021

Сельскохозяйс…Сельскохозяйственные организации из них: малые…из них: малые предприятия Хозяйства нас…Хозяйства населения Крестьянские …Крестьянские (фермерские) хозяйства и индивидуальные предприниматели 2022 2021 2022 в % к 20…2022 в % к 2021 Russian Federation Russian Federation / Сельскохозяйственные организации: 0.01 Russian Federation / из них: малые предприятия: 0.00 Russian Federation / Крестьянские (фермерские) хозяйства и индивидуальные предприниматели: 0.01 Russian Federation / 2022: 0.02 Russian Federation / 2021: 0.02 Russian Federation / 2022 в % к 2021: 70.2 Southern Federal District Southern Federal District / Крестьянские (фермерские) хозяйства и индивидуальные предприниматели: 0.01 Southern Federal District / 2022: 0.01 Volgograd Region Volgograd Region / Крестьянские (фермерские) хозяйства и индивидуальные предприниматели: 0.01 Volgograd Region / 2022: 0.01 North Caucasian Federal District North Caucasian Federal District / Сельскохозяйственные организации: 0.01 North Caucasian Federal District / из них: малые предприятия: 0.00 North Caucasian Federal District / 2022: 0.01 North Caucasian Federal District / 2021: 0.01 North Caucasian Federal District / 2022 в % к 2021: 80.6 Republic of Dagestan Republic of Dagestan / 2021: 0.00 Stavropol Territory Stavropol Territory / Сельскохозяйственные организации: 0.01 Stavropol Territory / из них: малые предприятия: 0.00 Stavropol Territory / 2022: 0.01 Stavropol Territory / 2021: 0.01 Stavropol Territory / 2022 в % к 2021: 96.2
how it will look

Set in numbers

The table is a snapshot — 6 series shown across 7 breakdowns. 21 values in total.

“Stavropol Territory” leads the table with 96.2, “Republic of Dagestan” closes it with 0.001. The gap between them is 96,154-fold.

All table numbers are series: 6
Series Сельскохозяйственные организациииз них: малые предприятияХозяйства населенияКрестьянские (фермерские) хозяйства и индивидуальные предприниматели202220212022 в % к 2021
Российская Федерация 0,01 0,00 — 0,01 0,02 0,02 70,25
Южный федеральный округ — — — 0,01 0,01 — —
Волгоградская область — — — 0,01 0,01 — —
Северо-Кавказский федеральный округ 0,01 0,00 — — 0,01 0,01 80,65
Республика Дагестан — — — — — 0,00 —
Ставропольский край 0,01 0,00 — — 0,01 0,01 96,15

How to read this table

Table rows divide data by geographic coverage, allowing comparison of federal-level indicators with those of a specific federal district and krai. Columns provide detail by type of agricultural producer, helping to understand which form of farming contributes most to the total sown area of the crop. By comparing values, one can assess not only the scale of production but also the rate of change in areas in 2022 relative to 2021 levels, expressed in percent

What is visible in these numbers

Only 50% of the cells carry a value — empty cells are missing in the source publication and are not filled in here.

The same indicator in other breakdowns (3)

More in the section Bulletins on the state of agriculture (electronic versions)»

Period and application

The information is current for 2022 and published in the Rosstat bulletin on the state of agriculture. The information will be useful to agro-industrial complex specialists, regional economy researchers, and analysts tracking the structure of sown areas for industrial crops. The data is presented as a snapshot, without breakdown over long time periods, making it a convenient tool for rapid analysis of the cotton growing situation for the reporting year