Continuation
The number of agricultural machinery available to farmers is determined by a total count of machines by type. This breakdown presents information on the fleet of grain harvesters and windrowers. The indicator demonstrates the technical equipment of farms, which directly affects harvest efficiency and the speed of field operations in different parts of the country. The data reflect the actual state of the machinery fleet as of the reporting date
Distribution of the number of grain harvesters and windrowers by constituent entity of the Russian Federation
Set in numbers
Rosstat data. Rows: 55, indicators: 7. Breakdown by items: Russian Federation; Volga Federal District; Central Federal District; Voronezh Oblast; Northwestern Federal District and others
Five highest values: Russian Federation (927), Volga Federal District (365), Siberian Federal District (198), Central Federal District (141), Southern Federal District (102). The gap between the highest and lowest is 309 times. The top five account for 77.1% of the total across all items — the distribution is heavily skewed towards the top
All table numbers are series: 55
| Series | Комбайны: — зерноуборочные | Комбайны: — кукурузоуборочные | Комбайны: — кормоуборочные | Комбайны: — льноуборочные | Комбайны: — картофелеуборочные | Свеклоуборочные машины (без ботвоуборочных) | Жатки валковые |
|---|---|---|---|---|---|---|---|
| Российская Федерация | 927,00 | 14,00 | 156,00 | — | 9,00 | 7,00 | 642,00 |
| Центральный федеральный округ | 141,00 | — | 36,00 | — | — | — | 91,00 |
| Брянская область | — | — | — | — | — | — | 5,00 |
| Владимирская область | 3,00 | — | 3,00 | — | — | — | — |
| Воронежская область | 19,00 | — | 4,00 | — | — | — | 20,00 |
| Ивановская область | — | — | 9,00 | — | — | — | — |
| Костромская область | — | — | 3,00 | — | — | — | — |
| Курская область | 12,00 | — | — | — | — | — | 3,00 |
| Липецкая область | 8,00 | — | — | — | — | — | — |
| Московская область | 4,00 | — | 3,00 | — | — | — | — |
| Орловская область | 14,00 | — | — | — | — | — | 5,00 |
| Рязанская область | 18,00 | — | — | — | — | — | 6,00 |
| Смоленская область | 6,00 | — | — | — | — | — | — |
| Тамбовская область | 46,00 | — | — | — | — | — | 37,00 |
| Тульская область | 7,00 | — | — | — | — | — | — |
| Северо-Западный федеральный округ | 9,00 | — | 9,00 | — | — | — | 7,00 |
| Вологодская область | — | — | 4,00 | — | — | — | — |
| Ленинградская область | — | — | 3,00 | — | — | — | — |
| Южный федеральный округ | 102,00 | — | 5,00 | — | — | — | 71,00 |
| Республика Калмыкия | 4,00 | — | — | — | — | — | — |
| Краснодарский край | 33,00 | — | — | — | — | — | 6,00 |
| Волгоградская область | 31,00 | — | — | — | — | — | 40,00 |
| Ростовская область | 29,00 | — | — | — | — | — | 20,00 |
| Северо-Кавказский федеральный округ | 31,00 | — | 4,00 | — | — | — | 17,00 |
| Ставропольский край | 31,00 | — | — | — | — | — | — |
| Приволжский федеральный округ | 365,00 | 11,00 | 65,00 | — | 4,00 | — | 251,00 |
| Республика Башкортостан | 66,00 | — | 6,00 | — | — | — | 29,00 |
| Республика Марий Эл | 9,00 | — | — | — | — | — | 4,00 |
| Республика Мордовия | 24,00 | — | — | — | — | — | 6,00 |
| Республика Татарстан | 34,00 | — | — | — | — | — | 24,00 |
| Удмуртская Республика | 22,00 | — | 17,00 | — | — | — | 9,00 |
| Чувашская Республика | 22,00 | — | 4,00 | — | — | — | — |
| Пермский край | 8,00 | — | 3,00 | — | — | — | 4,00 |
| Кировская область | 15,00 | — | 9,00 | — | — | — | 3,00 |
| Нижегородская область | 19,00 | — | 10,00 | — | — | — | 7,00 |
| Оренбургская область | 49,00 | — | 3,00 | — | — | — | 67,00 |
| Пензенская область | 8,00 | — | — | — | — | — | — |
| Самарская область | 33,00 | — | — | — | — | — | 28,00 |
| Саратовская область | 36,00 | — | 4,00 | — | — | — | 43,00 |
| Ульяновская область | 20,00 | — | — | — | — | — | 15,00 |
| Уральский федеральный округ | 67,00 | — | 7,00 | — | — | — | 53,00 |
| Курганская область | 20,00 | — | — | — | — | — | 19,00 |
| Свердловская область | 6,00 | — | 4,00 | — | — | — | 4,00 |
| Тюменская область | 17,00 | — | — | — | — | — | 3,00 |
| Тюменская область (без автономных округов) | 17,00 | — | — | — | — | — | 3,00 |
| Челябинская область | 24,00 | — | — | — | — | — | 27,00 |
| Сибирский федеральный округ | 198,00 | — | 27,00 | — | — | — | 143,00 |
| Алтайский край | 62,00 | — | — | — | — | — | 45,00 |
| Красноярский край | 26,00 | — | 6,00 | — | — | — | — |
| Кемеровская область | 24,00 | — | — | — | — | — | 18,00 |
| Новосибирская область | 47,00 | — | 8,00 | — | — | — | 53,00 |
| Омская область | 23,00 | — | 3,00 | — | — | — | 17,00 |
| Томская область | 5,00 | — | — | — | — | — | — |
| Дальневосточный федеральный округ | 14,00 | — | 3,00 | — | — | — | 9,00 |
| Амурская область | 8,00 | — | — | — | — | — | — |
How to read this table
Analysis of the presented indicators allows for assessing differences in the technical base between federal districts. When studying the table, it is worth paying attention to the distribution of machinery, as production volumes and sown areas require different numbers of machines for optimal operation. Comparing rows helps identify regions with a high concentration of equipment and compare the scales of agricultural mechanization in the Central, Volga, and other federal districts
What is visible in these numbers
Set indicators: Combines: — grain; Combines: — maize; Combines: — forage; Combines: — flax; Combines: — potato; Beet harvesters (excluding leaf harvesters); Swath headers
75 out of 175 possible values are filled (42.9%). The data are sparse — for many items, observations were not conducted for the entire period, and summary totals for them should not be calculated
The same indicator in other breakdowns (47)
More in the section Bulletins on the state of agriculture (electronic versions)»
Period and application
The figures presented are a final snapshot as of the current moment without a breakdown by dynamic periods. Statistical data are regularly updated by Rosstat to ensure transparency in the agricultural sector. The information is useful for industry analysts, ministry specialists, investors, and heads of agricultural enterprises for making management decisions, assessing production capacity potential, and planning purchases of new equipment
How it is calculated
Sown areas show what will be in abundance and what will be scarce several months before this is reflected on the shelf
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