Number of combines, sugar beet harvesters and other equipment written off due to wear and tear
The variety of decommissioned machinery—from combine harvesters to sugar beet harvesters—is reflected in the columns of this table. The rows allow for comparison of indicators across the Russian Federation and by major federal districts. The data records the number of units in each machine category decommissioned during the year due to physical wear and tear of the equipment
Number of agricultural machinery units decommissioned due to wear by machine type and federal district
Set in numbers
Rosstat data. Rows: 57, indicators: 7. Table rows: Russian Federation; Central Federal District; Volga Federal District; Southern Federal District; North Caucasian Federal District and others
The five largest values: Russian Federation (1 217), Volga Federal District (369), Southern Federal District (225), Central Federal District (218), Siberian Federal District (183). The gap between the largest and the smallest is 173.9 times. The top five account for 76.4% of the total for all positions — the distribution is heavily skewed toward the top
All table numbers are series: 57
| Series | Комбайны: — зерноуборочные | Комбайны: — кукурузоуборочные | Комбайны: — кормоуборочные | Комбайны: — льноуборочные | Комбайны: — картофелеуборочные | Жатки валковые | Свеклоуборочные машины (без ботвоуборочных) |
|---|---|---|---|---|---|---|---|
| Российская Федерация | 1 217,00 | 22,00 | 332,00 | 15,00 | 54,00 | 568,00 | 45,00 |
| Центральный федеральный округ | 218,00 | — | 75,00 | 6,00 | 15,00 | 95,00 | 21,00 |
| Белгородская область | 5,00 | — | 5,00 | — | — | — | — |
| Брянская область | 25,00 | — | 4,00 | — | — | — | — |
| Владимирская область | 8,00 | — | 6,00 | — | — | — | — |
| Воронежская область | 7,00 | — | 11,00 | — | — | 17,00 | 3,00 |
| Ивановская область | 4,00 | — | — | — | — | — | — |
| Курская область | 21,00 | — | 7,00 | — | — | — | — |
| Липецкая область | 16,00 | — | 3,00 | — | — | 13,00 | — |
| Московская область | 7,00 | — | 12,00 | — | 6,00 | 4,00 | — |
| Орловская область | 46,00 | — | 3,00 | — | — | — | — |
| Рязанская область | 14,00 | — | 3,00 | — | — | 21,00 | — |
| Смоленская область | 5,00 | — | — | — | — | — | — |
| Тамбовская область | 26,00 | — | — | — | — | 14,00 | 5,00 |
| Тверская область | 7,00 | — | 4,00 | — | — | 4,00 | — |
| Тульская область | 15,00 | — | — | — | — | — | — |
| Ярославская область | 4,00 | — | 5,00 | — | — | — | — |
| Северо-Западный федеральный округ | 14,00 | — | 21,00 | — | — | 10,00 | — |
| Вологодская область | 9,00 | — | 6,00 | — | — | 5,00 | — |
| Новгородская область | — | — | 3,00 | — | — | — | — |
| Южный федеральный округ | 225,00 | 7,00 | 16,00 | — | — | 131,00 | 12,00 |
| Краснодарский край | 106,00 | — | 10,00 | — | — | 25,00 | — |
| Волгоградская область | 32,00 | — | — | — | — | 28,00 | — |
| Ростовская область | 71,00 | — | — | — | — | 71,00 | — |
| Северо-Кавказский федеральный округ | 92,00 | 7,00 | 4,00 | — | — | 10,00 | 4,00 |
| Республика Ингушетия | 8,00 | — | — | — | — | — | — |
| Ставропольский край | 79,00 | — | — | — | — | — | 4,00 |
| Приволжский федеральный округ | 369,00 | 5,00 | 135,00 | — | 17,00 | 197,00 | 7,00 |
| Республика Башкортостан | 27,00 | — | 14,00 | — | — | 25,00 | 3,00 |
| Республика Мордовия | 23,00 | — | — | — | — | — | — |
| Республика Татарстан | 51,00 | — | 21,00 | — | — | 4,00 | — |
| Удмуртская Республика | 33,00 | — | 18,00 | — | 8,00 | 13,00 | — |
| Чувашская Республика | 7,00 | — | 5,00 | — | — | 3,00 | — |
| Пермский край | 9,00 | — | 12,00 | — | — | — | — |
| Кировская область | 16,00 | — | 9,00 | — | — | — | — |
| Нижегородская область | 11,00 | — | 10,00 | — | 3,00 | — | — |
| Оренбургская область | 78,00 | — | 24,00 | — | — | 53,00 | — |
| Пензенская область | — | — | 3,00 | — | — | 5,00 | — |
| Самарская область | 32,00 | — | 9,00 | — | — | 41,00 | — |
| Саратовская область | 49,00 | — | — | — | — | 37,00 | — |
| Ульяновская область | 18,00 | — | 4,00 | — | — | 5,00 | — |
| Уральский федеральный округ | 48,00 | — | 27,00 | — | 9,00 | 18,00 | — |
| Курганская область | 10,00 | — | — | — | — | 9,00 | — |
| Свердловская область | 16,00 | — | 18,00 | — | — | — | — |
| Тюменская область | 12,00 | — | 4,00 | — | — | — | — |
| Тюменская область (без автономных округов) | 12,00 | — | 4,00 | — | — | — | — |
| Челябинская область | 10,00 | — | — | — | — | 6,00 | — |
| Сибирский федеральный округ | 183,00 | — | 50,00 | — | 3,00 | 78,00 | — |
| Алтайский край | 44,00 | — | 12,00 | — | — | 25,00 | — |
| Красноярский край | 32,00 | — | 10,00 | — | — | 16,00 | — |
| Иркутская область | 10,00 | — | 6,00 | — | — | — | — |
| Новосибирская область | 62,00 | — | 11,00 | — | — | 19,00 | — |
| Омская область | 27,00 | — | 5,00 | — | — | 11,00 | — |
| Дальневосточный федеральный округ | 68,00 | — | 4,00 | — | — | 29,00 | — |
| Забайкальский край | — | — | — | — | — | 12,00 | — |
| Приморский край | 13,00 | — | — | — | — | — | — |
| Амурская область | 32,00 | — | 4,00 | — | — | 7,00 | — |
How to read this table
Comparing rows helps to assess the scale of machinery fleet renewal in different parts of the country. Total figures for Russia can be compared with those of the Central or Volga Federal Districts to identify where machinery wear is most pronounced. Columns with specific machine models allow for viewing the structure of decommissioning by individual equipment types
What is visible in these numbers
The set contains the following indicators: Combines: — grain harvesters; Combines: — corn harvesters; Combines: — forage harvesters; Combines: — flax harvesters; Combines: — potato harvesters; Windrowers; Beet harvesters (excluding haulm harvesters)
Out of 175 possible values, 95 are filled (54.3%). There are many gaps: a significant portion of the positions were observed only in certain periods, and such series should be compared with caution
The same indicator in other breakdowns (3)
More in the section Bulletins on the state of agriculture (electronic versions)»
Period and application
Data is collected from electronic versions of agricultural status bulletins. Information is presented as unit values for each type of machinery. The materials are suitable for analyzing the state of the agro-industrial complex's material and technical base and assessing the rate of machinery retirement from service
How it is calculated
Data on crops, yields, and livestock are used in planning raw material purchases, assessing harvests, and forecasting food prices
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