Availability of tractors in agricultural organizations by region
The technical equipment level of the agro-industrial complex for 2024 and 2025 is reflected by the number of tractors per unit of arable land. The table contains 93 rows, including indicators for the entire country, federal districts, and individual constituent entities. Comparing these values allows for an assessment of equipment distribution efficiency among farms across different parts of the country. The data covers key agricultural regions and provides a cross-section for the specified years
Chart of tractor availability for agricultural organizations by region for 2024–2025
Set in numbers
Rosstat data for 2024–2025. Rows: 186, periods: 2. Shown by items: Russian Federation — Tractors per 1,000 ha of arable land; Russian Federation — Arable land per tractor, ha; Central federal district — Tractors per 100…; Central federal district — Arable land per tractor, ha; Belgorod Region — Tractors per 1,000 ha of arable land and others
Kaluga Region — Arable land per tractor, ha: the indicator increased from 417.1 to 424.5 for 2024–2025 — by 1.78%. Of the 25 largest rows, 12 increased, 13 decreased
All table numbers are series: 186
How to read this table
Rows in the table are categorized by territory: they include aggregate figures for the Russian Federation, data for the Central Federal District, and, for example, the Belgorod Region. Analyzing these rows allows for a comparison of mechanization levels at both the national and regional scales. This facilitates identifying gaps in equipment availability between major agricultural hubs and medium-sized regions using indicators from the last two years
What is visible in these numbers
Five highest values: Kaluga Region — Arable land per tractor (424.5), Kostroma Region — Arable land per tractor (417.2), Russian Federation — Arable land per tractor (372.2), Ivanovo Region — Arable land per tractor (353.5), Lipetsk Region — Arable land per tractor (351.7). The gap between the highest and lowest is 177.1 times. The top five account for 48.1% of the total for all items
Observations are annual and cover 2 periods. Cells with numbers: 50 out of 50 (100%); data gaps are virtually absent
The same indicator for other periods
These series cannot be stitched together Over the common period 2024 values diverge — Rosstat recalculated the indicator using a revised methodology. They cannot be combined into a single chart: a break would appear at the junction, which did not actually occur
| Indicator for 2024 | here | in the adjacent series | divergence |
|---|---|---|---|
| Российская Федерация — Приходится тракторов на 1000 га пашни, штук | 2,7 | 2,7 | +0,0 |
| Российская Федерация — Приходится пашни на один трактор, га | 368,4 | 368,4 | −0,0 |
| Центральный федеральный округ — Приходится тракторов на 1000 га пашни, штук | 3,0 | 3,0 | +0,0 |
| Центральный федеральный округ — Приходится пашни на один трактор, га | 337,8 | 337,8 | +0,0 |
| Белгородская область — Приходится тракторов на 1000 га пашни, штук | 3,1 | 3,1 | +0,0 |
| Белгородская область — Приходится пашни на один трактор, га | 325,5 | 325,5 | +0,0 |
| Брянская область — Приходится тракторов на 1000 га пашни, штук | 3,9 | 3,9 | +0,0 |
| Брянская область — Приходится пашни на один трактор, га | 254,6 | 254,6 | +0,0 |
| Владимирская область — Приходится тракторов на 1000 га пашни, штук | 4,5 | 4,5 | +0,0 |
| Владимирская область — Приходится пашни на один трактор, га | 220,7 | 220,7 | +0,0 |
| Воронежская область — Приходится тракторов на 1000 га пашни, штук | 3,4 | 3,4 | +0,0 |
| Воронежская область — Приходится пашни на один трактор, га | 291,7 | 291,7 | +0,0 |
More in the section Bulletins on the state of agriculture (electronic versions)»
Period and application
Information is published as part of agricultural status bulletins. The 2024 and 2025 data cross-section is relevant for planning equipment procurement and assessing the state of farm machinery fleets. These statistics are useful for agronomists, agricultural enterprise managers, and agricultural economics specialists monitoring technical infrastructure
How it is calculated
Data on sown areas, yield and livestock are used in planning raw material procurement, crop assessment and food price forecasting
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