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Share of sown areas of sugar beet in farms of different

· snapshot · rows: 32 · periods: 5 · section: Bulletins on the state of agriculture (electronic versions)

Distribution of sugar beet area by farm type is presented in percent. Rows include data for the country, federal districts, and individual regions, allowing for comparison of crop structures across regions. Data covers all legal forms, from all-category farms to peasant and farm enterprises and individual entrepreneurs

shown 25 largest of 32

Chart of the share of sugar beet sown area by farm type in Russian regions, percent

Russian Federation 100.0 Central Federal District 100.0 Belgorod Region 100.0 Voronezh Region 100.0 Kursk Region 100.0 Belgorod Region 100.0 Krasnodar Territory 100.0 Volga Federal District 100.0 Republic of Mordovia 100.0 Republic of Tatarstan 100.0 Chuvash Republic 100.0 Nizhny Novgorod Region 100.0 Oryol Region 100.0 Tambov Region 100.0 Tula Region 100.0 Southern Federal District 100.0 Rostov Region 100.0 North Caucasus Federal District 100.0 Karachay-Cherkess Republic 100.0 Chechen Republic 100.0 Stavropol Territory 100.0 Republic of Bashkortostan 100.0 Penza Oblast 100.0 Saratov Region 100.0 Ulyanovsk Region 100.0
how it will look

Set in numbers

This is a cross-section: 32 series across 5 breakdowns. 134 values in total.

The largest value belongs to “Saratov Region” — 36.8; the smallest is “Oryol Region” at 0.484. The gap between them is 76-fold.

All table numbers are series: 32
Series Хозяйства всех категорийСельскохозяйственные организациииз них: малые предприятияХозяйства населенияКрестьянские (фермерские) хозяйства и индивидуальные предприниматели
Российская федерация 100,00 91,14 9,51 0,16 8,70
Центральный федеральный округ 100,00 95,01 5,40 0,29 4,70
Белгородская область 100,00 96,03 1,07 0,09 3,89
Брянская область 100,00 999 999 999,00 — — 9,94
Воронежская область 100,00 999 999 999,00 5,30 999 999 999,00 7,24
Курская область 100,00 96,70 5,50 0,01 3,29
Липецкая область 100,00 93,49 2,93 0,08 6,43
Орловская область 100,00 99,52 4,53 — 0,48
Рязанская область 100,00 100,00 13,42 — —
Тамбовская область 100,00 96,44 10,37 — 3,56
Тульская область 100,00 97,61 13,79 — 2,39
Южный федеральный округ 100,00 85,82 11,38 — 14,18
Краснодарский край 100,00 87,09 10,75 0,00 12,91
Волгоградская область 100,00 999 999 999,00 — — —
Ростовская область 100,00 999 999 999,00 19,73 — 30,17
Северо-кавказский федеральный округ 100,00 86,30 23,36 — 13,71
Республика Ингушетия 100,00 93,75 — — 6,25
Карачаево-Черкесская Республика 100,00 64,31 18,96 — 35,69
Чеченская Республика 100,00 97,41 90,43 — 2,59
Ставропольский край 100,00 88,15 15,54 — 11,86
Приволжский федеральный округ 100,00 86,40 16,99 0,03 13,57
Республика Башкортостан 100,00 76,57 22,45 — 23,43
Республика Мордовия 100,00 97,23 17,09 0,12 2,65
Республика Татарстан 100,00 91,18 16,75 0,03 8,79
Чувашская Республика 100,00 95,45 95,45 1,32 3,23
Нижегородская область 100,00 84,36 999 999 999,00 0,01 15,64
Оренбургская область 100,00 999 999 999,00 999 999 999,00 — —
Пензенская область 100,00 90,70 2,66 — 9,30
Саратовская область 100,00 999 999 999,00 46,72 — 36,78
Ульяновская область 100,00 72,22 56,38 — 27,78
Сибирский федеральный округ 100,00 98,30 0,80 — 1,70
Алтайский край 100,00 98,30 0,80 — 1,70

How to read this table

Rows differ by territorial coverage, ranging from the Russian Federation level to specific constituent entities such as the Belgorod Region. Columns show the share of sugar beet by ownership and management type. Comparing these indicators allows for assessing the share of crops held by large enterprises versus small-scale farmers and private producers

What is visible in these numbers

The table is 90% filled: Rosstat does not publish every cell, and the gaps are left as they are rather than interpolated.

The same indicator in other breakdowns (1)

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

Period and application

Statistics are compiled as part of agricultural bulletins. Information is provided as a single-point snapshot without time-series data. This information is useful for agribusinesses, farmers, and agricultural market analysts to assess the structure of sown areas