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Official Rosstat statistics turned into charts instead of CSV and XLSX

RU

Sugar beet in all categories of farms and farm enterprises

· snapshot · rows: 31 · periods: 4 · section: Bulletins on the state of agriculture (electronic versions)

Sugar beet sown area is distributed by ownership type, ranging from large enterprises to peasant and farm enterprises. The columns present data for all farm categories as well as separate data for individual entrepreneurs and farmers. The list includes the whole of Russia, federal districts, and individual subjects, including the Belgorod Region, allowing for an assessment of the cropping structure in different parts of the country

shown 25 largest of 31

Distribution of sugar beet sown area by farm type: all categories and farm enterprises

Belgorod Region 108.6 Tula Region 98.5 Oryol Region 98.3 Belgorod Region 98.1 Republic of Tatarstan 97.1 Central Federal District 96.4 Voronezh Region 94.9 Chechen Republic 94.8 Rostov Region 93.6 Russian Federation 93.1 Bryansk Region 92.8 Stavropol Territory 92.5 Saratov Region 92.5 Nizhny Novgorod Region 92.4 Kursk Region 92.3 Chuvash Republic 91.1 North Caucasus Federal District 90.8 Southern Federal District 90.7 Krasnodar Territory 90.7 Tambov Region 87.4 Penza Oblast 87.2 Volga Federal District 87.0 Republic of Mordovia 79.6 Republic of Bashkortostan 75.1 Karachay-Cherkess Republic 73.5
how it will look

Set in numbers

31 series, arranged across 4 breakdowns rather than over time. 103 values in total.

“Kursk Region” leads the table with 109, “Karachay-Cherkess Republic” closes it with 56.1.

All table numbers are series: 31
Series Хозяйства всех категорийСельскохозяйственные организацииХозяйства населенияКрестьянские (фермерские) хозяйства и индивидуальные предприниматели
Российская федерация 93,15 93,31 99,77 91,35
Центральный федеральный округ 96,36 96,41 99,96 95,04
Белгородская область 98,12 99,19 98,29 72,47
Брянская область 92,82 93,02 — 91,60
Воронежская область 94,89 94,69 100,00 96,98
Курская область 92,35 91,85 100,00 108,62
Липецкая область 108,64 109,57 100,00 95,33
Орловская область 98,33 98,36 — 92,79
Рязанская область 96,64 96,64 — —
Тамбовская область 87,44 87,15 — 95,95
Тульская область 98,53 98,67 — 95,00
Южный федеральный округ 90,73 90,22 95,01 93,80
Краснодарский край 90,66 90,27 95,01 93,26
Волгоградская область 62,12 62,12 — —
Ростовская область 93,56 92,26 — 96,73
Северо-кавказский федеральный округ 90,81 91,99 — 84,25
Карачаево-Черкесская Республика 73,53 88,81 — 56,07
Чеченская Республика 94,84 94,67 — 100,00
Ставропольский край 92,54 92,13 — 95,55
Приволжский федеральный округ 87,03 87,32 91,74 85,18
Республика Башкортостан 75,10 75,20 — 74,71
Республика Мордовия 79,62 79,55 90,65 81,28
Республика Татарстан 97,09 98,71 90,00 86,23
Чувашская Республика 91,07 91,32 99,96 86,60
Нижегородская область 92,43 97,17 100,00 60,78
Оренбургская область 19,24 19,24 — —
Пензенская область 87,19 86,81 — 90,67
Саратовская область 92,54 91,99 — 93,76
Ульяновская область 90,43 89,71 — 92,07
Сибирский федеральный округ 94,62 94,39 — 107,15
Алтайский край 94,62 94,39 — 107,15

How to read this table

The rows in the list vary by territorial coverage, from the total for the Russian Federation to specific federal districts and regions. By comparing indicators across different rows, one can see the share of farm enterprises in the total sown area within a specific region or district. The columns allow for a comparison of the total sugar beet area with the number of hectares allocated to this crop by small and medium-sized enterprises

What is visible in these numbers

Only 88% 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 (5)

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

Period and application

Data is taken from electronic bulletins on the state of agriculture. The information is presented as a one-off snapshot without time-series data, providing a view of the current area distribution. The data will be useful to agronomists, investors, and representatives of the processing industry for assessing the raw material base