New domestic agricultural machinery acquired over the year (continued)
Purchase volumes of domestic agricultural machinery are broken down by machine type, from combine harvesters to windrowers. The rows provide indicators for the entire Russian Federation, as well as for the Volga and Siberian Federal Districts separately. This information allows for an assessment of the agro-industrial complex's equipment levels for specific machine types within a single reporting year
Number of units of new domestic agricultural machinery purchased by machine type
Set in numbers
Rosstat data. Rows: 58, indicators: 7. The breakdown is by the following positions: Russian Federation; Volga Federal District; Siberian Federal District; Central Federal District; Tambov Oblast and others
The five largest values are: Russian Federation (1 430), Volga Federal District (359), Siberian Federal District (317), Central Federal District (243), Southern Federal District (182). The gap between the largest and the smallest is 238.3 times. The top five account for 75.2% of the total for all positions — the distribution is heavily skewed toward the top
All table numbers are series: 58
| Series | Комбайны: — зерноуборочные | Комбайны: — кукурузоуборочные | Комбайны: — кормоуборочные | Комбайны: — льноуборочные | Комбайны: — картофелеуборочные | Свеклоуборочные машины (без ботвоуборочных) | Жатки валковые |
|---|---|---|---|---|---|---|---|
| Российская Федерация | 1 430,00 | 9,00 | 209,00 | — | 18,00 | — | 663,00 |
| Центральный федеральный округ | 243,00 | — | 30,00 | — | — | — | 109,00 |
| Белгородская область | 13,00 | — | — | — | — | — | 11,00 |
| Брянская область | 16,00 | — | — | — | — | — | 6,00 |
| Воронежская область | 37,00 | — | — | — | — | — | 8,00 |
| Калужская область | 7,00 | — | — | — | — | — | — |
| Курская область | 18,00 | — | — | — | — | — | 8,00 |
| Липецкая область | 26,00 | — | — | — | — | — | 29,00 |
| Московская область | 11,00 | — | 10,00 | — | — | — | — |
| Орловская область | 19,00 | — | — | — | — | — | — |
| Рязанская область | 22,00 | — | — | — | — | — | 7,00 |
| Смоленская область | 3,00 | — | — | — | — | — | — |
| Тамбовская область | 49,00 | — | 3,00 | — | — | — | 23,00 |
| Тверская область | 5,00 | — | — | — | — | — | — |
| Тульская область | 8,00 | — | — | — | — | — | — |
| Северо-Западный федеральный округ | 16,00 | — | 17,00 | — | — | — | 10,00 |
| Республика Коми | — | — | 3,00 | — | — | — | — |
| Вологодская область | 4,00 | — | 5,00 | — | — | — | — |
| Ленинградская область | 7,00 | — | — | — | — | — | — |
| Южный федеральный округ | 182,00 | — | 7,00 | — | — | — | 75,00 |
| Краснодарский край | 49,00 | — | 3,00 | — | — | — | 7,00 |
| Волгоградская область | 34,00 | — | — | — | — | — | 35,00 |
| Ростовская область | 93,00 | — | — | — | — | — | 27,00 |
| Северо-Кавказский федеральный округ | 212,00 | — | — | — | — | — | 125,00 |
| Ставропольский край | 109,00 | — | 4,00 | — | — | — | 24,00 |
| Приволжский федеральный округ | 359,00 | 5,00 | 97,00 | — | — | — | 156,00 |
| Республика Башкортостан | 40,00 | — | 10,00 | — | — | — | 12,00 |
| Республика Марий Эл | 7,00 | — | 7,00 | — | — | — | 4,00 |
| Республика Мордовия | 27,00 | — | 5,00 | — | — | — | — |
| Республика Татарстан | 48,00 | — | 8,00 | — | — | — | 22,00 |
| Удмуртская Республика | 32,00 | — | 20,00 | — | — | — | 7,00 |
| Чувашская Республика | 6,00 | — | 6,00 | — | — | — | 4,00 |
| Пермский край | 13,00 | — | 14,00 | — | — | — | 6,00 |
| Кировская область | 17,00 | — | 7,00 | — | — | — | — |
| Нижегородская область | 7,00 | — | 9,00 | — | — | — | 5,00 |
| Оренбургская область | 25,00 | — | 4,00 | — | — | — | 25,00 |
| Пензенская область | 20,00 | — | — | — | — | — | 10,00 |
| Самарская область | 77,00 | — | 3,00 | — | — | — | 23,00 |
| Саратовская область | 28,00 | — | — | — | — | — | 14,00 |
| Ульяновская область | 12,00 | — | — | — | — | — | 18,00 |
| Уральский федеральный округ | 68,00 | — | 13,00 | — | — | — | 24,00 |
| Курганская область | 18,00 | — | — | — | — | — | 10,00 |
| Свердловская область | 9,00 | — | 10,00 | — | — | — | 4,00 |
| Тюменская область | 35,00 | — | — | — | — | — | — |
| Тюменская область (без автономных округов) | 35,00 | — | — | — | — | — | — |
| Челябинская область | 6,00 | — | — | — | — | — | — |
| Сибирский федеральный округ | 317,00 | — | 40,00 | — | 5,00 | — | 153,00 |
| Республика Хакасия | 5,00 | — | — | — | — | — | — |
| Алтайский край | 71,00 | — | 6,00 | — | — | — | 48,00 |
| Красноярский край | 57,00 | — | 6,00 | — | — | — | 19,00 |
| Иркутская область | 5,00 | — | — | — | — | — | — |
| Кемеровская область | 28,00 | — | — | — | — | — | 10,00 |
| Новосибирская область | 108,00 | — | 17,00 | — | — | — | 57,00 |
| Омская область | 25,00 | — | 6,00 | — | — | — | 11,00 |
| Томская область | 18,00 | — | 3,00 | — | — | — | — |
| Дальневосточный федеральный округ | 33,00 | — | — | — | — | — | 11,00 |
| Приморский край | 5,00 | — | — | — | — | — | — |
| Амурская область | 18,00 | — | — | — | — | — | — |
How to read this table
Comparing rows helps identify which regions and districts purchase more machinery and which focus on specific equipment types. You can compare the number of combine harvesters with the number of windrowers to see the machinery fleet renewal structure. Differences between the national figure and data for individual federal districts clearly demonstrate regional specifics in the consumption of engineering products
What is visible in these numbers
Table columns: Combines: — grain harvesters; Combines: — corn harvesters; Combines: — forage harvesters; Combines: — flax harvesters; Combines: — potato harvesters; Beet harvesters (excluding haulm harvesters); Windrow headers
There are 78 values in the table out of 175 possible, which is 44.6%. There are many gaps — some positions were observed only in specific periods, which should be taken into account when making comparisons
The same indicator in other breakdowns (3)
More in the section Bulletins on the state of agriculture (electronic versions)»
Period and application
This information is relevant for agricultural machinery market analysis and procurement planning. Data on machine types and territorial districts helps assess machinery fleet renewal dynamics in agriculture. The data is collected as part of agricultural status bulletins and covers fifty-eight different types of machinery across the country
How it is calculated
Data on sowing, crop yields, and livestock are used in planning raw material purchases, assessing harvests, and forecasting food prices
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