The classification of agricultural producers by legal-organizational form (agricultural enterprises, peasant (family farms), household plots and gardening associations), traditionally used by the Russian official statistics, is outdated and masks the dynamic changes that have taken place. Due to the lack of output and sales data in 2016 agricultural census, the paper uses some assumptions to calculate the so called “standard revenue” as a measure of the potential output in each census farm. The results highlight that there is only a small share of commercial production units in Russia and there is high heterogeneity of agricultural producers within each legal-organizational farm type. Contrary to a priori expectations, a large number of household plots became commercialized between the previous census in 2006 and the latest census in 2016 and they contribute 19% of the standard revenue of all commercial census units, more than the share of family farms. These results suggest that the old classification used for statistical purposes does not reflect adequately the dynamic changes stemming from the response to market signals.

agricultural census, agrarian structure, farm classification in Russia.

Historically in Russia’s statistical system, farms are classified into four legal-organizational forms:

• • agricultural enterprises (corporate farms);
• • peasant farms (family farms);
• • household plots (subsistence farms of rural residents that produce mainly for family consumption and sell only the surplus);
• • gardening associations (small subsistence plots of mainly urban residents).

The classification by legal-organizational form is a legacy of the Soviet system. Many researchers have concluded, however, that the legal-organizational form is not a good measure of farm size. We see from Table 1 that the maximum size of peasant farms is ten times greater than the average size of agricultural enterprises; even among household plots the maximum size is substantially greater than the average size of either agricultural enterprises or peasant farms. Some peasant farms in Rostov and Saratov oblasts control more than 40,000 hectares of land and are thus bigger than some agroholdings. There is an orders-of-magnitude disparity in the land size of farms of different types. Agricultural enterprises average more than 3,000 hectares of agricultural land, while peasant farms average about 300 hectares and household plots or plots in gardening associations have substantially less than 1 hectare of agricultural land per farm. Agricultural enterprises and peasant farms account for just 0.5% of the total number of agricultural producers, and the whole farm size distribution is strongly skewed by the numerical predominance of very small farms (Table 1).

Table 1Download as CSV 

Descriptive statistics of the size of farms of various types by agricultural land, 2016 (quantiles, median, and mean per farm in hectares).

Ag land	Min	5%	25%	Median	Mean	75%	95%	Max
Ag enterprises	0.0	0.0	128	953	3187.0	3422	13 587	341 092
Peasant farms	0.0	0.0	9	60	329.6	270	1490	40 443
Household plots	0.0	0.01	0.05	0.10	0.52	0.21	0.77	16 073
Gardening associations	0.0	0.01	0.02	0.04	0.05	0.05	0.09	84.3

Source: Rosstat (2017).

Agricultural Census 2016 made it possible to obtain a large array of data, on the basis of which a new classification of farms by size can be proposed. In international practice, farms are typically classified by size, with size measured by land area, by value of output, or by sales revenue.¹Land as a physical size measure does not reflect the farm’s operations mix. Information about the value of output is notoriously difficult to collect in surveys and, although sales revenue is an incomplete measure of size as it corresponds only to the part of the output that the farmer decides to sell, it provides the best approximation when the value of output is unavailable. Indeed, in the United States, farms are traditionally classified by the annual sales revenue and so-called economic classes of farms are defined by sales thresholds. The Russian census (in 2006 and in 2016) did not collect information about either output or sales revenue. Yet sales revenue is available in the annual reports filed by a subset of Russian farms — some 20,000 agricultural enterprises or corporate farms (SKhO — selskokhozyaistvennye organizatsii in Russian).

In this article, we tried to devise a farm classification based on sales revenue, similar to the U.S. system (USDA, 2019), but had to overcome the difficulty created by the missing sales revenue for farms other than agricultural enterprises. To this end, the so-called standard revenue methodology was used;² standard revenue was calculated and new farm classification by economic activity was made.

Our methodology uses two sources of data: (1) the agricultural census (2006 and 2016) that covers all farms (agricultural producers) in Russia and presents strictly quantitative (non-monetary) information — excluding quantities produced; (2) annual reports of agricultural enterprises (corporate farms) consolidated regionally and nationally, with monetary information about production costs and sales revenue. None of the sources provides price information or information about the value of output.

The stratification by region in the calculation of standard revenue is particularly important as there is considerable variability across regions in both standard crop revenue per hectare and standard livestock revenue per head, which would be lost had we used national averages for revenue. For example, the standard crop revenue in rubles per hectare per year ranged from a maximum of 34,500 in Krasnodar Kray to less than 4,000 in Kirov and Magadan oblasts, Perm and Trans-Baikal krays, and a number of Siberian ethnic republics. The standard livestock revenue similarly varied from a high of 152,700 rubles per head per year in Tula Oblast to a low of less than 50,000 rubles per head per year in a number of Caucasian and Siberian republics. These regional order-of-magnitude differences in crop and livestock revenue per standard unit are naturally carried over to the calculation of per farm standard revenue, as outlined above.

The classification of agricultural producers by standard revenue has led to a breakdown into so-called economic activity classes, which is a refinement of the USDA breakdown into economic sales classes. The Russian farms were grouped into three activity classes by standard revenue in U.S. dollar equivalents:⁵

• • residential and recreational farms with standard revenue up to $300 per year (approximately 10,000 rubles for the 2006 census and 20,000 rubles for the 2016 census);
• • subsistence family farms with standard revenue between $300 and $1,000 per year (between approximately 10,000 rubles and 30,000 rubles for the 2006 census and between 20,000 rubles and 60,000 rubles for the 2016 census);
• • commercial farms with standard revenue greater than $1,000 per year (approximately 30,000 rubles per year for the 2006 census and 60,000 rubles per year for the 2016 census).

In the USDA classification, a farm is defined as “any place from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the year” (USDA, 2019, p. VIII). The two lowest economic activity classes — residential and subsistence farms — fall outside the USDA definition of a farm, which corresponds to the class of commercial farms in the proposed classification. The results of classification are presented in Table 2.

Table 2Download as CSV 

Structure of economic activity classes by legal-organizational form in the 2016 Agricultural Census.

	All census farms	Farms without agricultural production	Agricultural producers
			residential & recreational farms	subsistence farms	commercial farms
Number of farms, thousands	35 866.8	7405.1	22 383.6	2903.4	3174.7
All census farms by legal form, %
Enterprises	0.1	0.1	0*	0*	0.8
Peasant farms	0.5	0.8	0.1	0.2	3.3
Household plots	64.4	48.9	60.9	96.3	95.8
Gardening associations	35.0	50.2	39.0	3.5	0.1
Total	100	100	100	100	100
Agricultural land, thousand hectares	141 011.5	7558.3	2181.2	1236.3	130 035.7
Agricultural land by legal form, %
Enterprises	63.5	53.0	0.4	0.8	65.8
Peasant farms	27.9	25.7	1.2	3.2	28.6
Household plots	8.3	21.3	77.7	95.0	5.6
Gardening associations	0.3	0	20.7	1.0	0.0
Total	100	100	100	100	100
Standard revenue, billion rubles	3 930.4	0	108.5	99.9	3 722.0
Standard revenue by legal form, %
Enterprises	63.0		0.0	0.0	66.5
Peasant farms	13.8		0.0	0.2	14.6
Household plots	22.3		68.5	97.2	18.9
Gardening associations	0.9		31.5	2.6	0.0
Total	100		100	100	100

Note: * 0 stands for a negligible number. Source: Rosstat (2017).

Most of the commercial farms (96% of the group or 3.2 million producers) are household plots; the group also includes about 100,000 peasant farms and 40,000 agricultural enterprises (Table 2). Furthermore, the commercial household plots generate 18,9% of the standard revenue produced by all commercial farms, more than the share of peasant farms and second only to the category of agricultural enterprises. This goes directly against the prevailing view of household plots as subsistence oriented, non-commercial operations. Dividing the household plots into two groups — subsistence and commercial — we note that the commercial household plots are characterized by a much larger area of agricultural land and much larger sown area (Table 3). Commercial plots also have a much larger livestock herd and achieve a much higher standard revenue (see Table 3).

Table 3Download as CSV 

Characteristics of subsistence and commercial household plots, 2016.

	Subsistence household plots	Commercial household plots
Number of farms, thousands	2837.1	3041.1
Agricultural land per farm, hectares	0.42	2.38
Sown area per farm, hectares	0.13	0.46
Standard area, standard hectares	1.36	3.29
Standard headcount, standard head	0.17	2.32
Standard revenue per farm, thousands rubles	34.71	231.60

Source: Rosstat (2017).

A different perspective is gained by examining the change between 2006 and 2016 Agricultural Censuses (Rosstat 2007, 2017) in structure of legal-organizational forms by economic activity classes (Table 4). Most agricultural enterprises and peasant farms are commercial farms. Most household plots (and practically all units in gardening associations) are residential farms. A significant change is observed only for peasant farms: the proportion of commercial peasant farms increased markedly from 68% in 2006 to 92% in 2016. The proportion of commercial agricultural enterprises also increased between 2006 and 2016 but less dramatically in percentage terms. Household plots, on the other hand, shifted more toward residential farms, as many abandoned their commercial orientation.

Table 4Download as CSV 

Structure of legal-organizational farm types by economic activity classes in two censuses (%).

	2006					2016
	residential	subsistence	commercial	total		residential	subsistence	commercial	total
AgEnt	2.0	3.5	94.5	100		1.1	1.1	97.8	100
PF	24.9	6.9	68.3	100		3.8	3.8	92.4	100
HH	65.1	16.5	18.4	100		70.0	14.4	15.6	100
GAss	98.8	1.1	0.1	100		98.8	1.2	0.0	100

Note: AgEnt — agricultural enterprises; PF — peasant farms; HH — household plots; GAss — gardening associations. Sources: Rosstat (2007, 2017)

Table 5 presents the average farm sizes in 2006 and 2016 for farms of all legal-organizational types broken down by economic activity classes. The average farm size increased between 2006 and 2016 for all commercial and residential farms, while subsistence farms generally shrank in size (Fig. 1). Particularly noticeable is the increase of commercial household plots, where agricultural land per farm grew by more than 90% between 2006 and 2016 (see Table 5). We thus observe a certain polarization of farm sizes, with the middle category of subsistence farms disappearing and agricultural land flowing to the two extremes of larger (commercial) and smaller (residential) farms.

Table 5Download as CSV 

Average agricultural land per farm by statistical farm types and economic activity classes (hectares per farm).

	2006					2016
	residential	subsistence	commercial	all		residential	subsistence	commercial	all
AgEnt	17.5	21.8	3010	2848		27.8	36.5	3261	3187
PF	3.4	15.0	213	147		6.2	9.2	356	330
HH	0.13	0.40	1.24	0.38		0.12	0.42	2.38	0.52
GAss	0.07	0.11	0.22	0.07		0.05	0.12	0.13	0.05

Sources: Rosstat (2007, 2017).

Fig. 1.

Changes of mean area of agricultural land per farm by activity class, 2006–2016 (%).

Source: Authors’ calculations based on Table 1 and Rosstat (2007).

Commercial farms are a dominant sector in Russian agriculture. They account for fully 95% of the standard revenue of agriculture and control nearly 98% of agricultural and sown area in Russia (Fig. 2). Performance changes in commercial farms may thus make a major contribution to overall agricultural performance.

While the physical stock of land in commercial farms remained fairly constant (the indicators for total land, agricultural land, and sown area), the so-called standard land area increased, boosting the share of commercial farms from 80% to nearly 90% of the total (see Fig. 2). This suggests transition to more intensive, and presumably higher value-added, crops in commercial farms, as the standard area differs from the physical area in that it allows for the cropping pattern and the national-average per hectare costs of the various crops.⁶

Fig. 2.

Share of commercial farms in six performance indicators, 2006 and 2016 (% of respective totals for Russia).

Note: St.Rev — standard revenue, Land — total land area (hectares), AgLand — agricultural land (hectares), St.ha — standard land area (standard hectares), St.head — standard animal headcount (standard head), Sown — sown area (hectares). Sources: Saraikin (2019); Table 2.

Fig. 3 shows the percentage changes in six performance indicators across the three economic activity classes (residential, subsistence, and commercial farms). The commercial farms are the only activity class that showed growth by four of the six indicators between 2006 and 2016: standard revenue, standard land area, standard animal headcount, and a marginal increase in sown area.

Fig. 3.

Changes of six performance indicators by economic activity class, 2006–2016 (%).

Note: St.Rev — standard revenue, Land — total land area (hectares), AgLand — agricultural land (hectares), St.ha — standard land area (standard hectares), St.head — standard animal headcount (standard head), Sown — sown area (hectares). Sources: Saraikin (2019); Table 2.

The area sown to technical crops in commercial farms increased by 52% between 2006 and 2016, and the area under vegetables grew by 72%, which boosted the standard area despite decreases in the physical area under potatoes (–12%) and perennial orchards (–30%). The standard area in residential and subsistence farms decreased due to the large across-the-board decrease in the sown areas for all crops in these activity classes. The standard animal headcount is also weighted by nationally averaged production costs per physical head, and the increase of this indicator for commercial farms was a direct outcome of the massive increase of about 50% between 2006–2016 in the number of more intensive livestock — pigs and poultry (the number of cows and beef cattle in this period decreased by about 15%). By comparison, the physical headcount of all animal species in residential and subsistence farms decreased quite dramatically (by about 50% and more) between 2006–2016, which naturally resulted in the observed decrease in the standard headcount in these economic activity classes (see Fig. 3). The commercial farms are now the main livestock producers in Russia, controlling about 98% of the herd (by standard headcount). The livestock production in non-commercial farms has been marginalized since 2006.

In the USDA classification by economic sales classes, farms with less than $1,000 are ignored. In the Russian classification by economic activity classes, $1,000 is the threshold between non-commercial and commercial farms. Non-commercial farms with standard revenue up to $1,000 are divided into residential and subsistence family farms, as discussed above, while the commercial farms with standard revenue of more than $1,000 can be subdivided into four subgroups that also follow the USDA classification by economic sales class:⁷

• • Subsidiary family farms with standard revenue between $1,000 and $10,000 (first economic sales class in the USDA classification)
• • Peasant farms with standard revenue between $10,000 and $100,000 (second economic sales class)
• • Small capitalist farms with standard revenue between $100,000 and $1 million (third, fourth, and fifth economic sales classes in the USDA classification combined)
• • Large capitalist farms with standard revenue of more than $1 million (the top economic sales class in the USDA classification)

The growth of nominal standard revenue between 2006 and 2016 shows a clear upward trend with average farm size (Fig. 4): it is lowest for residential and subsistence farms (80% and 60%, respectively), higher for peasant farms and small capitalist farms (170%), and highest for large capitalist farms (460%). However, as noted previously, the consumer price index rose to 240 in 2016 from the base of 100 in 2006, and it is only the large capitalist farms that showed a positive real growth of standard revenue between 2006 and 2016. For all other farms, the real (inflation-adjusted) standard revenue in 2016 was less than in 2006.

Fig. 4.

Growth of standard revenue, 2006–2016 (%).

Note: Changes in absolute values of standard revenue, without normalization as in Fig. 3. PF — peasant farms, SmlCap — small capitalist farms, LrgCap — large capitalist farms. Sources: Saraikin (2019); Tables 2–5.

All the other performance indicators (total land area, agricultural land area, standard area, standard animal headcount, and sown area) showed noticeable growth for large capitalist farms (40%–70% by most indicators) as opposed to general decline for other economic activity classes (Fig. 5).

Fig. 5.

Changes of physical indicators by economic activity classes, 2006–2016 (%): Growth by all indicators in large capitalist farms, decline in (almost) all other activity classes (exception: slight growth of total land in residential farms).

Note: St.Rev — standard revenue, Land — total land area (hectares), AgLand — agricultural land (hectares), St.ha — standard land area (standard hectares), St.head — standard animal headcount (standard head), Sown — sown area (hectares). Sources: Saraikin (2019); Tables 2–5.

The share of large capitalist farms in all performance indicators without exception markedly increased between 2006 and 2016 (Fig. 6), whereas the share of small capitalist farms decreased by most indicators (Fig. 7). The only exception in this group was the total land, where the share of small capitalist farms was smaller in 2006 than in 2016. Here again we observe that only the largest farms show a robust growth of their share, while the share of farms of other activity classes decreases.

Fig. 6.

Share of large capitalist farms in 2006 and 2016 (% of respective totals).

Note: St.Rev — standard revenue, Land — total land area (hectares), AgLand — agricultural land (hectares), St.ha — standard land area (standard hectares), St.head — standard animal headcount (standard head), Sown — sown area (hectares). Sources: Saraikin (2019); Table 5.

Fig. 7.

Share of small capitalist farms in 2006 and 2016 (% of respective totals).

Note: St.Rev — standard revenue, Land — total land area (hectares), AgLand — agricultural land (hectares), St.ha — standard land area (standard hectares), St.head — standard animal headcount (standard head), Sown — sown area (hectares). Sources: Saraikin (2019); Table 5.

Russia is a huge country spanning eleven time zones, with a great diversity of geographical and agro-climatic conditions. It is not surprising, therefore, that agricultural performance is highly variable across the 80-odd administrative divisions of the second level (oblasts, krays, republics; Fig. 8). As noted above, standard revenue per hectare and per head range between wide limits over Russia. This variability is preserved even when Russia’s regions are grouped into eight first-level administrative divisions — the federal districts (federalnye okruga), as in Fig. 8.

Fig. 8.

Variability of crop and livestock efficiency measures (standard revenue per hectare and standard revenue per head in thousand rubles) across regions grouped by federal district, 2016.

Source: Rosstat (2017).

The Central Federal District, which includes Moscow and the near-lying oblasts, has the highest performance by both revenue per standard hectare and revenue per standard head (Fig. 8). The Southern Federal District, which includes the strongly agricultural chernozem regions of Krasnodar and Rostov just south of the border with Ukraine, attains a high crop productivity by revenue per standard hectare, and so does the North Caucasus Federal District, where Stavropol is located. Yet their livestock performance is poor. The second highest livestock performance by revenue per standard head (after the Central Federal District) is recorded in the North-Western Federal District, where it is attributable to the Leningrad Oblast; its crop performance is poor, probably due to the harsh northern weather and low-quality soil.

Even the high performing federal districts have very low performing regions (Fig. 9). The variability of agricultural productivity thus cannot be fully explained by agro-climatic and geographical conditions. It is probably also due to differences in management capacity and lobbying power across regions. The regional variability is substantially greater in livestock production (see Fig. 5). This may be due to uneven entry of livestock agroholdings (such as Miratorg) across regions. Regions with agroholdings benefit from increased investment, including state support funds, and thus attain high efficiency. Regions without the investment boost from agroholding suffer a decline of the livestock herds and have correspondingly low livestock performance indicators. A possible relationship between regional performance and the entry of agroholdings is a subject of ongoing research.

Fig. 9.

One-way analysis of variance of crop and livestock efficiency measures by federal district, 2016: Revenue per standard hectare (left panel) and revenue per standard head (right panel), both in thousand rubles.

Note: The horizontal middle bar in each diamond is the mean, the two short horizontal bars near the apexes are the 95% confidence limits. Source: Rosstat (2017).

Analysis of the 2016 agricultural census (Rosstat, 2017) shows that Russian agriculture remains strongly dual after more than 25 years of policy reforms (this was discussed also in Lerman and Sedik, 2013). Out of a total of 28.5 million agricultural producers in the 2016 census, 25.3 million (or 89% of farms) produce just 5% of marketable agricultural output. On the other hand, commercial farms cultivate 92.2% of all agricultural land and receive 94.7% of all standardized revenue. Unexpected was the high share of household plots and family farms in total standard revenue — together 33.5%, which allows us to conclude that there is a high degree of commercialization among both forms. However, the number of commercial household plots is small — 3 million out of a total of 23,1 million, or just 13,2% of all household plots.

The strong duality by standard revenue is demonstrated in Fig. 10, where the long left tail of small farms abruptly gives way to a vertical “cliff” (defined by the horizontal black line) in which 11% of large farms produce 95% of marketable output.⁸ All the farms in the left tail are from the two lower activity classes: residential and subsistence farms; the farms in the right tail are from the top activity class of commercial farms (3.2 million farms, see Table 2). A similar manifestation of dual structure is provided by land, where 11% of relatively large farms (all of them commercial) control 97% of land (Rosstat, 2017).

Fig. 10.

Dual structure of Russian agriculture: Concentration of standard revenue in a small number of large farms, 2016.

Source: Rosstat (2017).

Analysis of the 2016 agricultural census highlights the heterogeneity of the agricultural producers within each legal-organizational farm type traditionally identified by Russian official statistics (agricultural enterprises, peasant farms, household plots, gardening associations). Agricultural enterprises and peasant farms produce the bulk of commercial agricultural output, while the commercial output of household plots is produced by a small subset of this huge category of farms. The classification of agricultural producers by legal-organizational form is outdated: some peasant farms produce more revenue than agricultural enterprises and some household plots match or exceed peasant farms by their production. A new farm classification system is required in the new environment, based on sales volumes of the activity class we have defined as commercial farms — some 3.2 million units or 9% of the census farms with annual sales of at least $1,000. This relatively small group of farms contributing 95% of agricultural revenue (see Table 2) and controlling 97% of agricultural land (see Fig. 2) should be at the focus of monitoring and policy efforts.

These results suggest that the traditional farm classification used for statistical purposes does not reflect adequately the dynamic changes stemming from the response to market signals. In view of the strong skewness of agrarian structure by both standard revenue and land it may be relevant to revisit the sampling criteria for statistical surveys, eliminating the lower tail of small, recreational and subsistence farms and thus bringing the Russian farm survey criteria more in line with the farm-classification criteria in the U.S. and the E.U.

The agrarian structure changed markedly during the decade 2006-2016, as the corporate farm sector — including the new agroholdings — began regaining the ground it had lost to the family-farm sector after 1990. This is a further indication of the development of market mechanisms in Russia’s agriculture. However, the decrease in the role of small agricultural producers constitutes a distinct danger to national food security and rural development, while the unchecked growth of super-large enterprises biases lobbying efforts, agricultural policies, and budget allocations in favor of the very large producers. To provide a level playing field for all agricultural producers, the government should strive, on the one hand, to ensure a more uniform distribution of agricultural subsidies and, on the other, to assist by every possible means in the development of alternative rural employment opportunities and in integrated development of rural areas. Development of alternative rural employment and rural infrastructure is crucial if we are to achieve the policy goal of supporting only sustainable and market-oriented producers. Subsistence farms should be supported through social budgets earmarked for the development of alternative sources of rural income, not by agricultural production budgets.

The development of massive agroholdings localized in certain regions has had an unexpected negative effect. The local agroholding becomes the sole employer in the region, and yet from considerations of efficiency it must shed labor: there are not enough jobs in the agroholdings for the entire population. Creation of new non-agricultural jobs is a function of infrastructure development and takes time. In the short term, the new rural unemployed either migrate to urban areas in search of employment or retreat into small-scale subsistence farming on their household plot to supplement their pensions. These negative effects play out with special force when the local agroholding goes bankrupt: an entire region is left without sources of employment and income, which leads to abandonment and depopulation as people move elsewhere in search of work. A rural revitalization program approved in June 2019 with a budget of 200 billion rubles annually (Government of the Russian Federation, 2019) is intended for government co-financing of large social-engineering projects in rural areas (roads, housing, water and electricity supply), but however important its potential contribution to rural infrastructure, no amount of comfortable modern housing can replace jobs. Policy makers must address alternative job creation in rural areas as a top priority.