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Official Rosstat statistics turned into charts instead of CSV and XLSX

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Share of long-staple flax sown areas by legal form of organisation

· snapshot · rows: 33 · periods: 5 · section: Bulletins on the state of agriculture (electronic versions)

The distribution of fiber flax sown area among different organizational and legal forms of holdings is presented in columns ranging from the total for all categories to peasant and farm holdings. Rows cover the Russian Federation, the Central Federal District, and the Ivanovo Region, allowing for a comparison of sowing structures at the national and regional levels. The indicators reflect the share of each farm type in the total area sown with the crop

shown 25 largest of 33

Share of fiber flax sown area by farm type, from all categories to farm holdings, Rosstat data

Russian Federation 100.0 Central Federal District 100.0 Ivanovo Region 100.0 Smolensk Region 100.0 Tver Region 100.0 Yaroslavl Region 100.0 Northwestern Federal District 100.0 Vologda Region 100.0 Pskov Region 100.0 Volga Federal District 100.0 Udmurt Republic 100.0 Nizhny Novgorod Region 100.0 Novosibirsk Oblast 100.0 Omsk Region 100.0 Bryansk Region 100.0 Vladimir Region 100.0 Siberian Federal District 100.0 Irkutsk Region 100.0 Kirov Region 100.0 Saratov Region 100.0 Altai Territory 100.0 Jewish Autonomous Region 100.0 Kursk Region 100.0 Tula Region 100.0 Moscow 100.0
how it will look

Set in numbers

The table is a snapshot — 33 series shown across 5 breakdowns. 87 values in total.

“Vladimir Region” leads the table with 100, “Yaroslavl Region” closes it with 3.7. The gap between them is 27.1-fold.

All table numbers are series: 33
Series Хозяйства всех категорийСельскохозяйственные организациииз них: малые предприятияХозяйства населенияКрестьянские (фермерские) хозяйства и индивидуальные предприниматели
Российская Федерация 100,00 66,55 35,56 — 33,45
Центральный федеральный округ 100,00 80,71 55,58 — 19,29
Брянская область 100,00 87,83 — — 12,17
Владимирская область 100,00 100,00 100,00 — —
Ивановская область 100,00 87,51 87,51 — 12,49
Курская область 100,00 — — — —
Смоленская область 100,00 72,57 29,80 — 27,43
Тверская область 100,00 69,25 40,19 — 30,75
Тульская область 100,00 — — — —
Ярославская область 100,00 96,31 96,31 — 3,69
г. Москва 100,00 — — — —
Северо-Западный федеральный округ 100,00 74,45 66,42 — 25,55
Вологодская область 100,00 74,58 71,19 — 25,42
Псковская область 100,00 73,89 44,39 — 26,11
Северо-Кавказский федеральный округ 100,00 — — — —
Республика Дагестан 100,00 — — — —
Приволжский федеральный округ 100,00 68,00 20,37 — 32,00
Республика Татарстан (Татарстан) 100,00 — — — —
Удмуртская Республика 100,00 69,88 17,24 — 30,12
Кировская область 100,00 100,00 — — —
Нижегородская область 100,00 48,94 33,49 — 51,06
Пензенская область 100,00 — — — —
Саратовская область 100,00 — — — 100,00
Сибирский федеральный округ 100,00 35,29 — — 64,71
Алтайский край 100,00 100,00 — — —
Иркутская область 100,00 100,00 100,00 — —
Кемеровская область - Кузбасс 100,00 — — — —
Новосибирская область 100,00 55,56 55,56 — 44,44
Омская область 100,00 3,48 3,48 — 96,52
Томская область 100,00 — — — —
Дальневосточный федеральный округ 100,00 — — — —
Амурская область 100,00 — — — —
Еврейская автономная область 100,00 — — — 100,00

How to read this table

Comparing rows helps to understand how fiber flax production is distributed between large enterprises and small-scale farmers. The table shows the difference between national figures and those of specific regions, such as the Ivanovo Region. To analyze the sowing structure, column data should be correlated with the territorial divisions in the rows

What is visible in these numbers

Only 63% 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 (2)

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

Period and application

The data is presented as a single snapshot without time-series links. The information is relevant for agricultural specialists, economists, and those analyzing crop production structures across various farm types. This data helps assess regional specialization and the efficiency of different organizational forms in industrial crop production