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RU

Share of crop production in agricultural holdings of various organizational and legal forms Sugar beet

· snapshot · rows: 31 · periods: 5 · section: Bulletins on the state of agriculture (electronic versions)

Sugar beet production is distributed among various farm types, from all categories to peasant and farm enterprises. Rows include the Russian Federation, federal districts, and individual constituent entities, allowing for comparison of regional shares of the total volume. Data is presented as a proportion and covers various legal forms without being tied to specific years

shown 25 largest of 31

Distribution of sugar beet production by farm type in Russia and its regions

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

Set in numbers

31 series, arranged across 5 breakdowns rather than over time. 126 values in total.

“Chuvash Republic - Chuvashia” leads the table with 71.2, “Oryol Region” closes it with 0.633. The gap between them is 112-fold.

All table numbers are series: 31
Series Хозяйства всех категорийСельскохозяйственные организациииз них: малые предприятияХозяйства населенияКрестьянские (фермерские) хозяйства и индивидуальные предприниматели
Российская Федерация 100,00 88,52 12,06 0,05 11,43
Центральный федеральный округ 100,00 93,41 7,66 0,09 6,49
Белгородская область 100,00 94,62 3,77 0,06 5,32
Брянская область 100,00 87,62 — — 12,38
Воронежская область 100,00 91,05 7,31 0,42 8,53
Курская область 100,00 93,68 7,55 — 6,32
Липецкая область 100,00 90,52 2,65 0,00 9,47
Орловская область 100,00 99,37 5,67 — 0,63
Рязанская область 100,00 100,00 60,18 — —
Тамбовская область 100,00 94,23 11,16 — 5,77
Тульская область 100,00 97,54 25,11 — 2,46
Южный федеральный округ 100,00 81,12 11,57 0,00 18,88
Республика Адыгея (Адыгея) 100,00 100,00 100,00 — —
Краснодарский край 100,00 82,46 11,00 0,00 17,54
Ростовская область 100,00 63,30 17,40 — 36,70
Северо-Кавказский федеральный округ 100,00 84,18 14,59 — 15,82
Кабардино-Балкарская Республика 100,00 100,00 — — —
Карачаево-Черкесская Республика 100,00 65,20 65,20 — 34,80
Ставропольский край 100,00 86,54 8,30 — 13,46
Приволжский федеральный округ 100,00 81,86 24,11 0,01 18,13
Республика Башкортостан 100,00 79,36 42,20 — 20,64
Республика Мордовия 100,00 96,47 22,89 0,06 3,48
Республика Татарстан (Татарстан) 100,00 83,75 14,14 0,02 16,22
Чувашская Республика - Чувашия 100,00 28,45 28,45 0,30 71,25
Нижегородская область 100,00 89,96 4,95 0,00 10,04
Оренбургская область 100,00 100,00 100,00 — —
Пензенская область 100,00 82,36 7,78 — 17,64
Саратовская область 100,00 64,26 46,31 — 35,74
Ульяновская область 100,00 64,48 43,20 — 35,52
Сибирский федеральный округ 100,00 100,00 — — —
Алтайский край 100,00 100,00 — — —

How to read this table

Table rows allow for comparing production structure at the national, federal district, and regional levels. Comparing indicators reveals the share of large enterprises versus small farms in beet cultivation. This provides insight into territorial specialization and the role of different ownership forms in the country's agricultural sector

What is visible in these numbers

Only 89% 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

Data is collected as part of agricultural monitoring and is available for analyzing the structure of the agro-industrial complex. The information is relevant for agricultural specialists, economists, and crop market researchers. Data on farm types helps assess the distribution of resources and capacities in crop production