From Peasant Subsistence to Industrial Farming: A Long-Term Analysis of Structural and Functional Changes in a Central Russian Agricultural Landscape

From Peasant Subsistence to Industrial Farming: A Long-Term Analysis of Structural and Functional Changes in a Central Russian Agricultural Landscape | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Peasant Subsistence to Industrial Farming: A Long-Term Analysis of Structural and Functional Changes in a Central Russian Agricultural Landscape Victor Matasov, Marina Kozyreva, Nikolai Surkov, Oleg Zheleznyy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7753587/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract The Anthropocene is characterized by profound land system changes, yet many studies focus solely on structural land cover transitions, overlooking the critical functional shifts within socio-ecological systems. Our study bridges this gap by integrating spatial analysis and socio-metabolic theory to examine the long-term evolution of an agricultural landscape in the forest-steppe of Central Russia from the mid-19th century to the present time. Using historical maps, satellite imagery, spatial statistics, and energy flow analysis based on historical archives and modern surveys, we quantify both the structural land use changes and the fundamental transformation of the land system's metabolism. Our results reveal a landscape of structural persistence, with the total area of arable land decreasing by only 19% over 160 years. However, this stability masks a radical metabolic shift. The mid-19th century biomass-based subsistence system was a closed-loop one, characterized by high energy efficiency, internal biomass recycling (46,980 GJ/year), and a land use pattern tightly coupled to settlement locations and human labor. In stark contrast, the modern industrialized system is entirely dependent on massive external fossil fuel inputs (1,503,748 GJ/year), has severed internal energy cycles, and exhibits a drastically reduced energy return on investment. Agricultural land use has decoupled from settlements and is now primarily determined by proximity to road infrastructure for machinery access. We conclude that the transition from the subsistence system to the industrialized one, driven by institutional and technological changes, has led to a severe decline in metabolic efficiency, despite increased yields. Our study underscores the critical importance of complementing spatial analysis with functional energy flow assessments to fully understand the sustainability of land systems and their long-term trajectories. socio-ecological systems agricultural landscape land system functioning EROI energy flows long-term dynamics land use change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The Anthropocene is marked by unprecedented land system change, driven by population growth and agricultural expansion (Ellis et al., 2010). From 1700 to 2000, the global share of landscapes transformed by humans surged from 10% to 60% (Ellis et al., 2010), with profound socio-ecological consequences (Dang and Kawasaki, 2017; Foley et al., 2005; Klein Goldewijk et al., 2010). This transformation has led to significant ecological trade-offs, including biodiversity loss, soil degradation, and altered biogeochemical cycles. Once-natural ecosystems have been converted into complex human-nature coupled systems, where socio-economic and ecological processes interact at multiple scales (Bourgeron et al., 2018). Long-term socio-ecological research (LTSER) serves as a prominent framework for comprehending the interplay between environmental and societal dynamics over extended time scales, emphasizing the need to analyze cumulative impacts and develop sustainable land use policies (Angelstam et al., 2019; Dick et al., 2018; Singh et al., 2010). While LTSER has been widely applied in Western Europe (Fischer-Kowalski and Weisz, 2016; Gingrich and Krausmann, 2018), Russia’s unique agrarian history, marked by serfdom, collectivization, and post-Soviet privatization, which have left distinct legacies on the land, offers a distinct context to test these methodologies (Matasov et al., 2019; Moon, 2013; Prishchepov et al., 2017). Theoretical frameworks of LTSER emphasize the need to integrate historical data with modern analyses to uncover patterns and drivers of landscape change (Bodin and Tengö, 2012). Most studies rely on satellite imagery to track land changes over short periods of time (typically decades), missing long-term dynamics and making attribution of underlying drivers difficult (Plieninger et al., 2016; Singh et al., 2013). Such studies often employ supervized classification and transition matrices to quantify shifts in land cover, while statistical methods help identify drivers like soil fertility, proximity to settlements and infrastructure (Prishchepov et al., 2013; Yin et al., 2018). Quantifying long-term land use shifts requires integrating remote sensing data (e.g., Landsat) with historical cartography (Fuchs et al., 2015; Kaim et al., 2014). While these data provide indirect insights, they rarely delve into people's actual land use decisions (Gutman et al., 2020; Hersperger et al., 2010; Munroe et al., 2019). Alternative approaches, such as agent-based models, which attempt to simulate land use choices, require extensive data and are often confined to fine-scale studies with pre-defined actors (Valbuena et al., 2010). Reviews by Plieninger et al. (2016) highlight the importance of combining GIS-based cartographic analysis with ethnographic data to capture socio-economic influences. Existing research tends to focus on macro-level changes like agricultural expansion or urban growth, neglecting finer-scale changes in management intensity, such as fertilizer use, machinery deployment, labor inputs, and yield fluctuations (Aspinall and Staiano, 2019; Erb, 2012; Thomson et al., 2019). This limits our ability to fully capture land use intensification pathways (Erb, 2012; Thomson et al., 2019). Additionally, abrupt events like economic crises, policy shifts, or wars trigger rapid land use changes, while slow-onset processes like population growth cause gradual transformations (Kanianska et al., 2014; Scheffer et al., 2001). Previous research has identified typical land use patterns and their drivers (Jepsen et al., 2015; Levers et al., 2018), but these studies often overlook the internal functioning of agricultural systems, focusing instead on spatial patterns and management approaches (Erb, 2012). While spatial analyses effectively document land-cover transitions, they often overlook functional changes in agroecosystems, such as shifts in energy flows or nutrient cycling. A structural shift from peasant farming to industrial agriculture, for example, may be characterized as persistent cropland on a map, yet it masks a radical transformation in system metabolism, from biomass recycling to fossil-fuel dependency (Galán et al., 2016; Krausmann, 2004). Socio-metabolic theories (Haberl, 2015) and energy balance frameworks (Gingrich and Krausmann, 2018; Tello et al., 2016) address this gap by quantifying inputs (e.g., labor, fertilizers) and outputs (e.g., crop yields). Land system science increasingly distinguishes between structural land-cover change (e.g., cropland expansion) and functional shifts in socio-metabolic flows (Marull et al., 2010; Meyfroidt, 2016). Previous works (Gingrich and Krausmann, 2018; Kuskova et al., 2008; Svirezhev et al., 1995; Tello et al., 2016) have established and further developed methodologies for reconstructing historical energy flows using agrarian statistics, but challenges persist. First, data scarcity, particularly for pre-industrial periods, often forces reliance on proxies (e.g., crop calorific values from analogous regions). Second, institutional changes (e.g., land reforms, political systems) can disrupt metabolic continuity or change the measurement units and statistical data collection system, complicating long-term comparisons, so data harmonization also becomes important (Aspinall and Staiano, 2019; Gingrich et al., 2016). Third, traditional energy balances in agriculture focus on external inputs and outputs, oversimplifying the complex internal dynamics of agroecosystems. Understanding intricate biophysical cycles, such as nutrient recycling and organic matter retention are crucial for grasping true energy dynamics. Thus, a more comprehensive approach is needed to accurately assess agricultural sustainability, incorporating multiple energy return on investment (EROI) metrics at different points within the system (Guzmán and González De Molina, 2015; Tello et al., 2016). Integrating spatial and metabolic analyses can provide a fuller picture of landscape-level sustainability in different regions or periods. The past 300 years have witnessed dramatic shifts in land use regimes across the globe, characterized by alternating phases of expansion, intensification, and abandonment (Jepsen et al., 2015; Ramankutty and Foley, 1999). In Russia, these transitions followed a distinct trajectory shaped by institutional shocks and socio-political upheavals. The pre-1861 serfdom era maintained a low-input, biomass-reliant land use regime, whereby peasant communities practiced extensive three-field rotations adapted to local environmental constraints. The abolition of serfdom initiated a gradual market integration phase, though land productivity remained limited by technological and climatic barriers (Alyabina et al., 2015). The Soviet period (1917–1991) marked a radical shift to a command-and-control regime, characterized by forced collectivization, mechanization, and input intensification, mirroring global trends but with unique ideological drivers (Jepsen et al., 2015; Weiner, 2000). Post-1991 reforms triggered a transitional regime of partial abandonment and agro-industrial consolidation, where market forces replaced central planning, yet legacy infrastructures persisted (Gutman and Radeloff, 2017; Prishchepov et al., 2012). Yet, Russia’s agroecological extremes (e.g., short growing seasons, low soil fertility in the northern territories) and spatial heterogeneity created land systems with distinct metabolic inefficiencies. Longitudinal analysis of these regimes underscores the need to integrate institutional, technological, and environmental drivers to explain nonlinear trajectories of change (Matasov et al., 2019). Here, we aimed to assess functional and structural changes in Central Russian agricultural landscapes from the 19 th to the 21 st centuries. To achieve it, we: - compared the spatial structure of the landscape identified from historical maps (19 th century) and satellite images (21 st century), - assessed spatially explicit determinants of land-cover change, and - reconstructed the energy flows using historical archives (19 th century) and stakeholder surveys (21 st century). By integrating these approaches, we bridged LTSER’s spatial and metabolic paradigms, while focusing on an understudied, yet historically significant region. 2. Materials and Methods 2.1. Study area and period Our research was conducted within the projected territory of the P.P. Semenov-Tyan-Shansky Estate Museum “Ryazanka” (https://usadba-ryazanka.ru/), located on the border of Ryazan and Lipetsk regions, between the Central Russian Upland and the Oka-Don Plain (Fig. 1). The area belongs to the forest-steppe biome, characterized by fertile chernozem soils, moderate continental climate (mean January temperature: −10°C; July: +19.5°C), and annual precipitation of ~550 mm (Krivtsov, 2008). The vegetation primarily consists of grasslands, with oak forests scattered throughout. The landscape comprizes three main natural elements: interfluves with Dnieper-age moraine and loess-like loams, now mostly cultivated; erosion networks with gullies and ravines; and the Ranova river valley in the northern part of the study area (Matasov et al., 2018). Since the 18th century, the region has specialized in agriculture due to the relatively fertile soils and favorable climatic conditions. The study area is historically significant as the home of geographer Pyotr Semenov-Tyan-Shansky, who Semenov carried out a census of the local rural community and collected statistical data within his own manor and its vicinity (Semenov, 1880). These data, along with Semenov’s comments and conclusions, provide deep insights into the everyday life of Central Russian peasants in the 19 th century. Our study uses the information from Semenov’s “Muraevenskaya volost” (republished in 2017) as the main source of historical statistical data (Semenov, 2017). Our study area, encompassing about 11,500 ha, was a part of the Muraevenskaya and Pitelino volosts (administrative unit level 3) of the Ryazan province (level 1) in the 19 th century. Today, this is the borderland between the Ryazan and Lipetsk regions. Around 10,000 people lived within the borders of the study area in the mid-19th century. Today, the population has decreased almost 10 times and amounts to 1181 people (Ryazanstat, 2017). Despite undergoing many changes related to administrative structures and settlement patterns over the last 200 years, these lands remain agriculturally active, which allowed us to compare pre-industrial and modern farming systems. We obtained information on land cover and land use, population and location-based characteristics (e.g. distances to roads and settlements) from several types of datasets: (a) historical land use records and topographic maps; (b) satellite imagery, including Sentinel-2 and Landsat-8 imagery from 2015 to 2020; (c) historical population and economic statistics as well as data from local agribusinesses that provided information on crop yields, machinery use, and fertilizer inputs; (d) a field-based landscape map at a scale of 1:20 000 (Matasov et al., 2018), characterizing the relief, vegetation and modern farming activities. 2.2 Reconstruction of land use/land cover To analyse the historical land use, we relied on the 1:84 000 Mende Atlas (1850) from the Center for Historical Geography and Cartography (www.etomesto.ru). Each map contained boundaries and information about individual land holdings (“dachas”), descriptive information about land ownership and land use types as well as notes with supplementary economic information (Matasov et al., 2019; Milov, 1965). The Mende maps had a preliminary georeferencing, but they contained some deviations, thus, during the manual digitization of the land use types, some elements, such as ravines and villages, were corrected if needed based the guidelines for working with such materials (Kusov, 1993; Matasov V., 2016). We used visual interpretation of Landsat-8 and Sentinel-2 imagery jointly with very-high-resolution imagery available from the Bing and Google Earth Web Map Service layers after 2015 to obtain land cover for the year 2020. Since the symbols on the Mende maps do not always correspond to the land types that exist today or the accepted terminology, a common legend was compiled for both time periods during digitization, enabling us to compare changes in land use patterns. Eleven land types were identified during the generalization process: arable land; dry meadows; floodplain meadows; fallow land; wet forests (black alder, willow, poplar nowadays, matched with sparse forest in the 19 th century); dry forests (oak, birch, aspen nowadays; matched with dense forest in the 19 th century); abandoned land - former arable land covered in weeds and bushes; residential areas with small household gardens; water bodies; quarries and industrial areas (absent in the 19 th century); land allocated for railways (absent in the 19 th century). 2.3 Land use determinants For a 30-meter regular grid covering the entire study area, we recorded types of land use in 1882 and 2019 (see above), distances to roads and settlements (calculated from historical and current OSM data using GDAL). Distance to settlements can be used as an indicator of the attendance of each pixel, considering the necessity of delivering manure to the fields (Milov, 1965; Semenov, 2017). The changes in land use relationship with settlement structure and road network were analyzed using R (Development Core R Team, 2011) 2.4 Energy flow analysis We adapted the Energy Return on Investment (EROI) framework (Tello et al., 2016) to compare agroecosystem efficiency in two periods: the middle 19 th century subsistence system and the modern industrialized system. Firstly, we determined the energy inputs and outputs in both periods. We used historical data on the main crops cultivated, their yields and the areas of arable land occupied by them, the number of livestock, the number of people employed in agriculture, and the ratio of areas under different types of land use (Semenov, 2017). Information on the current yield of each agricultural machinery and the number of workers was obtained from the agricultural company’s administration upon request. The local population size and number of animals kept on private farms were obtained from the municipal administration. The crop production in the 19 th century was calculated using data on the yield of rye and oats, their share of arable land, and field area (Semenov, 2017). Approximately 1/3 of the harvest went to livestock feed (Biomass Reused), the rest was exported outside the system boundaries. Another type of Biomass Reused - agricultural waste (straw) - was estimated at a 1:1 ratio to the harvest (Tello et al., 2016). When calculating livestock production, we chose cow milk, pork and lamb production only for comparability with modern data. Several assumptions were made: all cattle were assumed to be dairy cows due to lack of precise historical data; all sheep and pigs were counted toward meat production (assumed to be kept no longer than a year). Livestock waste (manure) was calculated based on the average daily excrement per animal (Kuzmin, 2012), accounting for dry matter content (Vasilev and Filippova, 1988). To estimate the livestock feed we used the annual demand of each livestock species in MJ. One third of feed came from arable land, the rest - from pastures and hayfield, considering land area, yield, and energy value of feed (Bulatov et al., 2016; Samojlov and Sechin, 2017). In the 19th century, labor energy included the power of horses and human labor. It was calculated based on the annual feed requirements for work and daily bread rations, as well as the size of the working population (Milov, 1965). Today such information is based on the human daily energy requirement (Kuzmin, 2012; Tello et al., 2016) and the number of employees of the agricultural holding, proportional to land area. The energy flow from the machinery utilized in modern times was estimated based on the number of machines and their annual working hours (Aguilera et al., 2015). Mineral fertilizers also represent an energy input, and were calculated using the company’s data on the volume of each type of fertilizer, proportional to the area of arable land in the key area. Finally, all flows (tons of crops/fertilizers, work hours, food calories, etc.) were converted to energy units. In the methodology proposed by Tello et al. (2016), such a unit is the gross calorific value (GCV), which shows how much heat is released during the complete combustion of 1 kg of a substance. However, for many flows, particularly those denoted through the amount of food or feed, nutritional energy value - the estimated amount of heat energy produced by a living organism when digesting the food eaten - can be applied. In our study the human/animal labor and feed were calculated via nutritional energy (kcal or MJ), all other flows (crops, waste, etc.) calculated via GCV (MJ/kg). In the table below we summarized all flows and units. Table 1. Methodology and sources for energy flows estimation. Flow Type Source of estimation methodology Unit Crops, straw (Guzmán and González De Molina, 2015; Milov, 1965) MJ/kg Manure (Sahu et al., 2016) kcal/kg →MJ/kg Fertilizers (Aguilera et al., 2015; Vasilev and Filippova, 1988) MJ/kg Machinery (Aguilera et al., 2015; Kuzmin, 2012) MJ/h Human/animal labor and feed (Kuzmin, 2012; Milov, 1965) kcal/MJ By converting all flows of the agroecosystem into joules, we could analyze its functioning. Solar energy, being essential for crop growth, biodiversity, and other processes, was excluded from these energy flow calculations, because it is a constant. The energy balance was always different from zero, as the analyzed agroecosystems are not closed: it receives inputs (fertilizers, feed, machinery) and exports outputs (crop and livestock products). In the 19 th century, internal flows included crop residues used as livestock bedding and animal waste recycled as organic fertilizer. Today, no such closed cycles exist and all waste leaves the system. Energy inputs now come entirely from external sources, independent of the agroecosystem’s internal mechanisms, while outputs are determined by societal demand. Thus, to assess energy efficiency of the system, we calculated the following indices: EFEROI (External Final Energy Return on Investment) = final output / Σ external inputs IFEROI (Internal Final Energy Return on Investment) = final output / reused biomass FEROI (Final Energy Return on Investment) = final output / reused biomass + Σ external inputs. 3. Results 3.1. Changes in Landscape Structure (1860–2020) 3.1.1 Land use structure in the19th century At the end of the 19 th century (1860-1880), the study area contained 14 settlements and 20 agrarian communes. Single-estate villages formed one commune, while multi-estate settlements had multiple communes based on differing land charters (reflecting varying peasant emancipation statuses). Most residents were serfs under corvée labor. Eight landed estates existed, with land owned either by nobles or communes. Communal land ownership emerged after abolition of serfdom, though 14 of 20 communes still experienced some serfdom practices. Peasants were managed by landowners or stewards, typically receiving small, irregularly-shaped plots adjacent to ravines. Semenov classified most households as marginal-to-poor (Semenov, 2017). The economy of Muraevenskaya volost was fundamentally agricultural, employing the traditional three-field system which provided both employment and subsistence for most residents. The rotation comprized: winter fields: rye (80-85%) with some winter wheat (15-20%); spring fields: predominantly oats (75-80%), supplemented by buckwheat (5-10%), millet (10%) and flax; and fallow field. Manure served as the sole fertilizer. Beyond agriculture, peasants engaged in supplementary industries: peat extraction, coal mining, brick/lime production, stonemasonry, metalworking, construction trades, flour milling, beekeeping, poultry farming, leatherworking, textiles, shoemaking, dyeing, and seasonal migrant labor. Agricultural income from allotted and private lands proved insufficient for redemption payments and taxes, necessitating these off-farm earnings (Semenov, 2017). Most of the territory (~8 thousand ha) was occupied by arable land (Fig. 2, top). 7% of the area (~1 thousand ha) was covered in forest. In economic notes of the 19 th century there were two types of forests differentiated not by the species but by possible use – for fuel (410 ha) and for constructions (570 ha). First one mostly located in wet valleys, and second one – on dry slopes of interfluves. The dry meadows were predominantly situated along gullies and ravines (1180 ha). Wet meadows and marshes occurred throughout the Ranova River valley (177 ha). Meadows were predominantly used for hay harvesting and grazing. Settlements with orchards and allotments occupied 515 ha. Beyond the river itself, water bodies included seasonal ponds in gully headwaters. Fields near Urusovo on Ranova's left bank lay fallow, while waterlogged areas surrounding villages remained uncultivated. 3.1.2. Land use structure in the 21st century Today, the territory comprizes two rural groups of settlements (similar to volost): Urusovsky (8 villages) and Miloslavskoye (19 villages). While most settlements have retained their 19 th century names, new ones have emerged, such as Yuzhny, founded as an administrative center during Soviet times. The area's primary land use remains largely unchanged – it persists as an agricultural landscape dominated by crop cultivation. The interfluve lands are under arable farming, where an agricultural holding cultivates winter and spring crops (wheat, barley, rapeseed) using a multi-field crop rotation system and their own machinery servicing several districts of Lipetsk region. Local residents are scarcely involved in agricultural operations, unlike 19 th century peasants. Their activities are limited to small-scale subsistence farming (poultry, pigs), small gardens near homes, and shared vegetable plots on village outskirts. Livestock grazing occurs informally on abandoned lands near settlements. The current land use structure has become more fragmented, while maintaining its fundamental patterns (Fig. 2, bottom). Watershed areas remain dominated by arable land (6645 ha), incorporating many erosion features. Dry meadows (1732 ha) still occupy slopes and bottoms of gullies, though their area has expanded due to gully growth. The upper sections of gullies have become overgrown with oak-birch forests, reducing arable land. Water bodies in catchment depressions have disappeared due to field levelling works. Floodplain meadows have given way to wet alder-willow woodlands. Abandoned lands overgrown with weeds and shrubs have increased significantly, particularly on abandoned arable land. Fallow fields have been largely replaced by completely abandoned lands, while residential areas have shrunk, with many villages vanishing entirely, replaced by forests and shrubs. Over the two centuries, the landscape underwent significant changes, yet the overall structure of land use remained largely intact (Table 1). Agriculture persists as the primary land use, with arable land still covering the largest area. However, the extent of cultivated land decreased by 19%, particularly in slopes near ravines and valleys—now replaced by dry forests and dry meadows (which expanded by 92% and 46%, respectively). Unused land also increased due to abandoned pastures and floodplain hayfields (down by 16%). With population decline, residential land use saw a sharp reduction (-71%). Former village sites are now predominantly abandoned land (+60%). Drainage of waterlogged depressions in upper ravine areas has reduced water body area by 75%. There has been a significant increase in fallow land (+181%) and floodplain forests (+94%) due to discontinued haymaking. Table 1. Change in land area from 1860 to 2020 Land Use Type Area in 1860 (ha) Area in 2020 (ha) % Change 1. Arable land 8196,2 6645,0 −19% 2. Dry meadows 1183,6 1732,3 +46% 3. Floodplain meadows 177,3 150,7 −16% 4. Fallow land 179,9 506,2 +181% 5. Wet/sparse forests 411,6 799,1 +94% 6. Dry/dense forests 571,0 1098,9 +92% 7. Abandoned land 236,0 376,9 +60% 8. Gardens & residential land 515,2 149,7 −71% 9. Water bodies 10,4 2,7 −75% 10. Quarries & excavations – 16,7 – 11. Railway land take – 66,3 – 3.2. Settlement and land use change The land use structure of the study area underwent significant reorganization between the 1860s and the 2020s, driven by a fundamental shift in the socio-economic and technological determinants of agricultural production. In the 19 th century, the spatial organization of land was a function of the peasant subsistence economy, which relied on human and animal labor. This created a landscape where proximity to settlements was the paramount factor. The need for farmers to walk to their fields daily resulted in a relatively uniform distribution of settlements across the study area, with some of them scattered across the interfluves (Fig. 2). Villages with adjacent household gardens were surrounded by intensively manured arable land and, where possible, floodplain meadows (Fig. 3a). The dense network of unpaved roads served to connect settlements to this radiating pattern of fields and meadows, with around three quarters of arable land lying within 500 m of a path (Fig. 3b). The road network likewise provided access to the forests, resulting in a fragmented natural landscape, yet a well-connected rural community. By the beginning of the 21 st century there was a significant decoupling of land use from human settlement patterns. Mechanization of agriculture abolished the walking distance constraint. Consequently, the influence of proximity to settlements diminished, resulting in abandonment of the majority of the interfluve, water-deficient villages. Arable fields and meadows are now further from the villages (Fig. 3a), whereas much of the area adjacent to remaining villages is either unused land, or floodplain forests providing recreational services. An even more significant change can be observed in the road network (Fig. 3b), with road to field median distance increasing more than twice, reflecting the use of heavy equipment that can traverse longer distances. Today, the most extensive and productive croplands are concentrated in sparsely populated interfluvial plateaus, operated by workers who commute rather than reside nearby. In summary, the land-use system transitioned from a settlement-centric, labor-intensive model to an infrastructure-dependent, capital-intensive model. This has led to a more fragmented landscape where the highest-productivity lands are maximized for machine-based cultivation, while less accessible or fertile areas are abandoned to ecological succession. 3.3. Energy Flow Dynamics 3.3.1. Energy indicators of 19 th century agroecosystem (closed-loop system) functioning Within the agroecosystem of Muraevenskaya volost, three blocks can be distinguished: the crop production block, which includes arable land; the livestock production block, which includes livestock as well as hayfields and pastures indirectly; and the forestry block. This paper focuses on the first two blocks because there is insufficient data on forestry production. Labor was provided by peasants living in the villages of the volost. The hayfield and pasture subsystems mainly interacted with livestock farming: pastures provided fodder for livestock in summer and hayfields in winter. The livestock was dominated by sheep (4205 heads), cows (1424) and pigs (398). Horses (1428) made up the main labor force. However, insufficient hayfields and pastures hindered the development of cattle breeding. The crop production area within the study area boundaries covered 8196 hectares. The entire arable area was divided into three parts due to the adopted three-field system. The oat yield was about 2.7 c/ha and the rye yield - 7.8 c/ha. The energy flow diagram (Fig. 4) illustrates the energy loops between the agroecosystem (subsystems of crop production and animal husbandry) and society. The largest energy flow comes from the crop production system, as represented by the rye and oat crops. This accounts for 27,557 GJ per year, which is harvested from the largest area within the agroecosystem. Some of this energy is converted into plant biomass when it is fed to livestock and consumed by people. The corresponding arrows move away from crop production. According to the scheme proposed by Tello et al. (2016), people employed in the agroecosystem are not part of it. This means that the energy from crop production that feeds them first leaves the boundaries of the agroecosystem and only then reaches the peasants. Therefore, the value of labor energy spent by peasants on agricultural work is not subtracted from crop production because the said production first goes outside the system boundaries. The livestock feed takes 12560 GJ from the harvested crop, i.e. a little less than half of the total energy of this flow. This is one third of the total annual energy that animals require. After deducting the output going to the livestock subsystem, the remaining crop production is equivalent to 14,997 GJ. This is a significant amount of crop production, as confirmed by the descriptions in 'Muraevenskaya volost': “ ...in general, the amount of rye produced by peasant lands gave a surplus against the need ”. A significant aspect of crop production was the generation of waste, with straw being used as animal bedding. With a ratio of 1:1 (Guzmán and González De Molina, 2015; Tello et al., 2016), the volume of waste (34,420 GJ) equaled that of the crop. The energy value of straw was higher than that of rye and oats due to its higher calorific value. This waste represented reused biomass remaining within the agroecosystem, thereby increasing its sustainability and reducing its dependence on external energy sources. The livestock subsystem received energy from arable land, hayfields and pastures. Hayfields (with herb and reed grass varieties) provided 56,325.7 GJ per year and their produce was stored for the winter months. Pastures (grain and legume-grass varieties) provided 32,861.6 GJ during the summer. The total energy output of livestock production (milk, pork and lamb) was 17,163.5 GJ. Manure, a waste product of livestock production, was distributed to crop production and associated biodiversity in equal flows of 73,018 GJ. In summer, livestock grazed on pastures and the manure remained there, fertilizing the soil and replenishing the biodiversity subsystem. In winter, it was collected, dried and transported to arable land. As Semenov-Tyan-Shansky noted, “ peasants take all the manure to the arable land without a rest ” (Semenov, 2017). Horses were a part of the livestock subsystem and served as the main labor force. Although their labor was formally directed towards crop production, it was expressed energetically through the feed they consumed, which was estimated at 84,109 GJ. Peasants involved in agriculture spent 4,598 GJ on their work per year. The total output of crop and livestock production is 32160.5 GJ. The predominance of crop production over livestock breeding is due, firstly, to the larger areas of arable land and, secondly, to the orientation of peasant farms of that time to crop production due to the availability of fertile soils. By analyzing energy flows, we calculated several energy efficiency metrics. External Final Energy Return on Investment (EFEROI) amounted to 6.99, meaning that the system outputted nearly 7 times more energy than it received from external inputs. The agroecosystem acted as a net energy donor to society—its production (primarily crop-based) was consumed externally, while energy inputs were minimal (mainly human labor, which requires relatively low energy investment). Internal Final EROI (IFEROI) was 0.68. The reused biomass (crop residues like straw and fodder) amounted to 49,980 GJ, compared to the final output of 32,160.5 GJ. This internal recycling reduced reliance on external resources, enhancing system resilience. Final EROI (FEROI) was 0.62. Since the final output is less than the sum of reused biomass and external inputs, this indicates efficient agroecosystem functioning. By maximizing internal resource cycling and minimizing external dependence, the system remains energy-sustainable while supplying surplus energy to society. 3.3.2. Modern Agroecosystem (Industrialized System) Today, the main subsystem is crop production, with crops being cultivated, harvested, and sold by the agricultural holding. The livestock subsystem, according to the administration’s comments, is represented by small-scale private farms of the local population and is incomparably smaller than crop production (Fig. 5). There are 63 units of cows, 147 sheep, 58 pigs, 1,850 poultry, 106 bee colonies in the private farms. Unlike the 19th century, when the main workforce consisted of local residents, today this contribution comes from outside through the agricultural holding, whose employees are partially engaged in the lands of the rural settlement. The local population (1181 people), however, does not participate in the agricultural holding’s farming activities and consumes all livestock products from their private farms themselves rather than selling them. In 2018, the agricultural holding harvested 5,116 tons of winter wheat, 3,568 tons of summer wheat, 5,576 tons of barley, and 3,132 tons of sunflower. It is worth noting that the crops grown on the fields vary from year to year. For example, in 2019, most fields were sown with rapeseed, spring barley, and spring wheat. The three-field system, which was common in the 19 th century, has been replaced by a more diverse multi-field approach. The configuration of the subsystems has also changed: the agroecosystem is now interacting with both villagers living within the study area and the agricultural holding as the main agent of agricultural production. The agricultural holding receives external inputs, fertilizers and machinery, directed toward crop production, which it manages. However, there is no connection between the agricultural holding and residents or private subsidiary farms (the equivalent of the livestock subsystem). Thus, the system has become open. The agricultural holding's crop (153,535 GJ) is completely exported from the fields without losses. Crop production waste (13,266 GJ) - crushed straw - left on the fields to decompose, becoming a variety of associated biodiversity. A high volume of crop production is provided by significant energy costs. In the modern agroecosystem, two energy flows have appeared that were absent in the time of Semenov-Tyan-Shansky: mineral fertilizers (1,422,117.2 GJ) and agricultural machinery (81,298.7 GJ). Machinery has replaced horses, and fertilizers have replaced organic matter. The energy of fertilizers is almost 100 times higher than the output. Human labor (333 GJ/year) is now represented by employees of the agricultural holding. The "local residents – private farms" subsystem includes two energy flows: the purchase of feed for livestock (4,488 GJ) and the receipt of livestock products (1,390 GJ – milk, lamb, pork). The feed is supplied mostly externally, due to small backyards for growing on household plots, which reduces the energy efficiency of the system itself. Although the analysis includes only basic products for comparison with the 19th century, agricultural enterprises also produce eggs (43 GJ), chicken (15 GJ), beef (39 GJ), goat's milk (298 GJ) and honey (32 GJ). However, even with these products, energy consumption exceeds the return, making the system energy unprofitable. In addition to products, animals also produce manure, which enters the associated biodiversity as livestock waste, since mineral fertilizers from the agricultural holding are used on the arable land. Local residents also contribute to this waste flow from their subsidiary farms, thus jointly this flow estimated at 11742 GJ. External Final Energy Return on Investment (EFEROI) amounted to 0.10.External energy inputs vastly exceed output (crop production only) value is 70 times less efficient than in 1860s. EFEROI equals total Final EROI (FEROI). The system lacks biomass reuse, as waste is disposed rather than recycled, making internal return (IFEROI) incalculable. Modern agroecosystem consumes substantially more energy than it produces. 3.3.3 Factors of changes in the functioning and energy efficiency of the agroecosystem Over two centuries, the general appearance of the study area has changed insignificantly, but its functioning has drastically transformed. The livestock and crop farming subsystems that existed in the 19th century have evolved. Traditional livestock farming has disappeared, replaced by small private household plots not aimed at full self-sufficiency. Crop farming has largely retained its previous form, but now both subsystems interact with associated biodiversity, whereas in the past, only livestock waste entered the ecosystem. Some elements of the agroecosystem, such as hayfields and pastures, have vanished. Although meadows (wet and dry) remain, they are no longer used for grazing. The human role has also changed: while peasants once worked the land and consumed its produce, arable land is now controlled by an agricultural holding that owns vast areas. As a result, human presence in the agroecosystem is limited to two separate groups: local rural residents and the agricultural holding, which coexist without intersecting. Changes in energy flows reflect shifts in the relationships between subsystems (table 2). For example, today, livestock farming and crop production are no longer interconnected, whereas in the 19th century they exchanged multiple flows: crop production provided fodder and straw, while livestock farming supplied organic fertilizers and the labor of horses for work in the fields. Another example of changing relationships is the shift in animal feed sources, from internal subsystems (arable land, pastures, hayfields) to external ones (as local residents now mostly purchase feed rather than extracting it from the agroecosystem). Table 2. Main indicators of changes in land system functioning Indicator 19 th century 21 st century Population 10039 1181 Number of livestock 1424 cattle 4205 sheep 1428 horses 398 pigs 63 cattle 147 sheep 58 pigs 1850 poultry 106 bee colonies Final production, GJ 32 160 153 535 Crop production, GJ 27 557 153 535 Livestock production, GJ 17 163 1390 Total external inputs 4598 1 503 748 Reused biomass 46 980 - Livestock waste 146 036 11 742 Crop waste 34 420 13 266 Livestock services 84 109 - Livestock feed inputs 101 747 4488 Total livestock inputs 136 167 4488 Total crop inputs 161 725 1 503 749 EFEROI (final production/external inputs) 6.99 0.10 IFEROI (final production /biomass reused) 0.68 - FEROI (final production/biomass reused + external inputs) 0.62 0.10 When discussing changes in the flows themselves, it is important to note the transition in external inputs from relying solely on peasant labor to the energy of fertilizers and agricultural machinery. Notably, total external inputs today are more than 300 times greater than in the 19th century. The main change in the amount of energy flows is due to the transformation of economic mechanisms: modern agriculture is increasingly dependent on external resources such as fertilizers and machinery, in contrast to traditional factors such as the use of horses and local labor. This has led to a significant increase in energy consumption in the former compared to the latter. The energy efficiency indicators of the agroecosystem have decreased. The external final energy return (EFEROI) dropped from 7 to 0.1 due to an imbalance between energy input and output: the modern system consumes more than it produces, making it less efficient than in the 19 th century. 4. Discussion The observed decline in energy efficiency of agricultural systems in the Muraevenskaya volost reflects a global trend yet demonstrates distinctive features characterizing Russia's agroecological and socioeconomic context. Our analysis reveals that while the EFEROI index decreased from 6.99 to 0.10 over the study period - mirroring patterns documented in Western Europe (Galán et al., 2016; Tello et al., 2016) - the Russian case was marked by significantly lower baseline productivity. Comparing our energy efficiency results with literature data from Russia and eslewhere, we see a general trend: a decrease in energy efficiency with an increase in production volumes. For example, the agroecosystem of Catalonia (Tello et al., 2016) is similar to ours in area, but due to a milder climate and a long growing season, it produced 3 times more energy in the 19 th century (EFEROI = 21.53) than it was invested in. However, its dependence on external resources was higher (IFEROI = 1.08). By 21 st century, the indicators had decreased both there and in Russia: EFEROI — to 0.25, IFEROI — to 2.20, FEROI — to 0.22. According to (Fuzella, 2009), the EFEROI for the Tomsk Region in Siberia is even lower, at 0.14. This confirms that modern agricultural systems require more energy than they return, which is consistent with the theory of increasing system complexity and energy consumption (Marull et al., 2010; Puzachenko et al., 2011). This phenomenon can be attributed to the complex interplay of severe climatic constraints, including short growing seasons that limited biomass accumulation, and institutional barriers such as serfdom's land management practices. The climatic conditions of the forest-steppe zone, particularly cold winters that slowed natural decomposition processes, created a unique "low-productivity trap" that continues to influence modern agricultural systems in the region (Lyuri et al., 2010). These findings suggest that climate-institutional interactions have played a more significant role in shaping Russian agricultural efficiency than previously recognized, with implications for understanding similar frontier regions globally. The limitations of relying solely on structural land-cover analysis become particularly apparent when examining the Muraevenskaya case. While traditional metrics might suggest relative stability through the 19% reduction in cropland area, our energy flow analysis reveals fundamental transformations in system functioning. The peasant agricultural system of the 1860s maintained tight nutrient cycles within local area, recycling 46,980 GJ/year of biomass, whereas contemporary systems depend overwhelmingly on external fossil fuel inputs totaling 1,503,748 GJ/year. This metabolic transition occurred despite superficial continuity in land cover patterns, underscoring the importance of complementing spatial analyses with functional assessments (Haberl, 2015). The discrepancy between spatial proxies and actual system functioning highlights the potential for misinterpretation when relying exclusively on land cover change data, particularly in regions undergoing complex socioeconomic transitions. Our findings align with growing recognition in land system science that intensification pathways must be evaluated through both spatial and metabolic lenses to fully understand sustainability trade-offs. Several critical knowledge gaps emerged from our analysis, particularly regarding the role of forest ecosystems and grassland productivity in historical agricultural systems (Beug, 1967; Krausmann et al., 2012; Moon, 2013). Based on rural economy descriptions (Semenov, 2017) we can suggest that woodland resources can contribute up to 15-20% of total energy inputs during the peasant period, comparable to patterns observed in the Alps (Bolliger et al., 2017; Gingrich and Krausmann, 2018), yet detailed reconstruction of “forestry metabolism” remains challenging due to data limitations. Similarly, uncertainties persist in quantifying historical grassland productivity, particularly in interpreting traditional hay yield measures such as the "kopna" unit, which could vary two- to threefold depending on local conditions (Milov, 1965). These knowledge gaps point to the need for innovative methodological approaches combining ethnographic research with paleoecological proxies to better reconstruct historical land use practices (Novenko et al., 2017; Poska et al., 2014; Seddon et al., 2014). Future studies in comparable regions could benefit from incorporating dendrochronological data and soil charcoal analysis to complement archival records, providing a more comprehensive understanding of long-term ecosystem dynamics. The institutional evolution of land management in the Muraevenskaya volost reveals path dependencies with important implications for sustainable transitions. From the constraints of serfdom through the collectivization period to contemporary agribusiness dominance, Russian agriculture has consistently prioritized production over circularity, resulting in persistent neglect of biomass recycling principles. This trajectory lays along with Western European experiences where institutional frameworks start to discuss sustainability considerations after wide ecological negative impacts (Pe’er et al., 2020; Stoate et al., 2009). Potential pathways forward could draw on historical adaptation strategies while incorporating modern technologies, such as combining elements of traditional multifunctional land use and precision agriculture techniques. Our findings challenge conventional intensification narratives by demonstrating that yield increases achieved through metabolic simplification may come at unacceptable sustainability costs (Biggs et al., 2015; Thomson et al., 2019). The Muraevenskaya case study suggests that truly sustainable agricultural systems require metabolic transparency across entire production chains, from field to fork, combined with institutional frameworks that value circularity. As Russia faces increasing climate variability and global market fluctuations, the lessons from this 150-year trajectory gain urgency. Future research should focus on developing regionally adapted models that reconcile productivity goals with energy efficiency, drawing on both traditional knowledge and technological innovations (Marull et al., 2010). Such approaches will be critical not only for Russia's agricultural future but for similar grain-exporting regions worldwide facing comparable sustainability challenges. 5. Conclusion Metabolic Over Structural Change: The most significant transformation from the 19 th to the 21 st century was not the change in land cover, which remained relatively stable, but the fundamental shift in the system's socio-metabolic functioning. Efficiency-Return Trade-off: The transition from a biomass-based peasant system to a fossil-fuel-driven industrial regime resulted in a drastic thousand-fold decline in energy efficiency (EFEROI fell from 6.99 to 0.10), despite an increase in absolute production output. Decoupling from Local Constraints: A primary driver of change was the decoupling of agricultural production from local socio-ecological factors. Mechanization and depopulation eliminated the constraint of walking distance, replacing it with a dependency on external inputs and road infrastructure. Loss of Circularity: The modern agroecosystem is characterized by broken internal cycles. The critical flows of biomass reuse and organic fertilization that sustained the historic system have been replaced by linear inputs of fossil energy and minerals. Policy Implications: Sustainability assessments cannot rely solely on spatial land cover data but must integrate functional metabolic analysis. Policies aiming for sustainable agricultural futures should prioritize reintegrating circular economy principles and reducing dependence on external inputs, rather than pursuing further intensification through non-renewable resources. Declarations Acknowledgments. We would like to thank Bogdanov A.A., researcher at the Semenov-Tyan-Shansky Museum, for providing archival information and assistance in organizing field research, including interviews with an agricultural company and a municipal administration. We would also like to express our gratitude to our colleagues Juan Marull, Roc Padro, and Enric Tello for their advice on calculating the energy balance and other aspects of the research. Funding. These studies were supported by a grant from the Ministry of Science and Higher Education of the Russian Federation (agreement NO. 075-15-2024-554 of 24 April 2024). Author Contributions Conceptualization, V.M.; methodology, V.M. and M.K.; software, N.S. and O.Z.; formal analysis, M.K., N.S. and O.Z.; investigation, M.K., O.Z. and V.M.; resources, M.K.; data curation, M.K., N.S.; writing—original draft preparation, V.M.; writing—review and editing, V.M., M.K. and O.Z.; visualization, V.M. and M.K.; supervision, V.M.; project administration, V.M.; funding acquisition, V.M. All authors have read and agreed to the published version of the manuscript. Conflicts of Interest The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results. References Aguilera, E., Guzmán, G.I., Infante-Amate, J., García-Ruiz, R., Herrera, A., Villa, I., Carranza, G., de Molina, M.G., 2015. EMBODIED ENERGY IN AGRICULTURAL INPUTS. INCORPORATING A HISTORICAL PERSPECTIVE. 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Land Use Policy 30, 873–884. https://doi.org/10.1016/j.landusepol.2012.06.011 Prishchepov, A.V., Radeloff, V.C., Baumann, M., Kuemmerle, T., Müller, D., 2012. Effects of institutional changes on land use: agricultural land abandonment during the transition from state-command to market-driven economies in post-Soviet Eastern Europe. Environ. Res. Lett. 7, 024021. https://doi.org/10.1088/1748-9326/7/2/024021 Puzachenko, Y.G., Sandlersky, R.B., Svirejeva-Hopkins, A., 2011. Estimation of thermodynamic parameters of the biosphere, based on remote sensing. Ecological Modelling 222, 2913–2923. https://doi.org/10.1016/j.ecolmodel.2011.05.011 Ramankutty, N., Foley, J.A., 1999. Estimating historical changes in global land cover: Croplands from 1700 to 1992. Global Biogeochemical Cycles 13, 997–1027. https://doi.org/10.1029/1999GB900046 Ryazanstat, 2017. Agricultural and socio-economic statistics 1990-2017. Territorial bureau of the federal state statistics service for the Ryazan region. Sahu, P.K., Chakradhari, S., Dewangan, S., Patel, K.S., 2016. Combustion Characteristics of Animal Manures. JEP 07, 951–960. https://doi.org/10.4236/jep.2016.76084 Samojlov, K.N., Sechin, V.A., 2017. Composition, nutritional value, and digestibility of feed (Sostav, pitatel’nost’ i perevariemost’ kormov), 2nd ed. Scientific center OGAU, Orenburg. Scheffer, M., Carpenter, S., Foley, J.A., Folke, C., Walker, B., 2001. Catastrophic shifts in ecosystems. 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Looking forward through the past: identification of 50 priority research questions in palaeoecology. Journal of Ecology 102, 256–267. https://doi.org/10.1111/1365-2745.12195 Semenov, P.P., 2017. Muraevensky volost. A collection of materials for studying the rural land community in Russia (with a preface by L.I. Zemtsov). Humanities researches of the Central Russia 3, 10–48. https://doi.org/10.24411/2541-9056-2017-00001 Semenov, P.P., 1880. Muraevenskaya volost: A Collection of Materials for the Study of the Rural Land Community in Russia (Muraevenskaya volost’ sbornik materialov dlya izucheniya sel’skoj pozemel’noj obshchiny v Rossii) (in russian). Sankt-Petersburg. Singh, S.J., Haberl, H., Chertow, M., Mirtl, M., Schmid, M. (Eds.), 2013. Long Term Socio-Ecological Research: Studies in Society-Nature Interactions Across Spatial and Temporal Scales. Springer Netherlands, Dordrecht. https://doi.org/10.1007/978-94-007-1177-8 Singh, S.J., Haberl, H., Gaube, V., Grünbühel, C.M., Lisivieveci, P., Lutz, J., Matthews, R., Mirtl, M., Vadineanu, A., Wildenberg, M., 2010. Conceptualising Long-Term Socio-ecological Research (LTSER): Integrating the Social Dimension, in: Müller, F., Baessler, C., Schubert, H., Klotz, S. (Eds.), Long-Term Ecological Research. Springer Netherlands, Dordrecht, pp. 377–398. https://doi.org/10.1007/978-90-481-8782-9_26 Stoate, C., Báldi, A., Beja, P., Boatman, N.D., Herzon, I., Van Doorn, A., De Snoo, G.R., Rakosy, L., Ramwell, C., 2009. Ecological impacts of early 21st century agricultural change in Europe – A review. Journal of Environmental Management 91, 22–46. https://doi.org/10.1016/j.jenvman.2009.07.005 Svirezhev, Y.M., Brovkin, V.A., Denisenko, E.A., 1995. Agroecosystem Analysis Approach Based on the Flows of Artificial Energy and Information. Tello, E., Galán, E., Sacristán, V., Cunfer, G., Guzmán, G.I., González De Molina, M., Krausmann, F., Gingrich, S., Padró, R., Marco, I., Moreno-Delgado, D., 2016. Opening the black box of energy throughputs in farm systems: A decomposition analysis between the energy returns to external inputs, internal biomass reuses and total inputs consumed (the Vallès County, Catalonia, c.1860 and 1999). Ecological Economics 121, 160–174. https://doi.org/10.1016/j.ecolecon.2015.11.012 Thomson, A.M., Ellis, E.C., Grau, Hé.R., Kuemmerle, T., Meyfroidt, P., Ramankutty, N., Zeleke, G., 2019. Sustainable intensification in land systems: trade-offs, scales, and contexts. Current Opinion in Environmental Sustainability 38, 37–43. https://doi.org/10.1016/j.cosust.2019.04.011 Valbuena, D., Verburg, P.H., Bregt, A.K., Ligtenberg, A., 2010. An agent-based approach to model land-use change at a regional scale. Landscape Ecol 25, 185–199. https://doi.org/10.1007/s10980-009-9380-6 Vasilev, V.A., Filippova, N.V., 1988. Handbook on Organic Fertilizers (Spravochnik po organicheskim udobreniyam). Rosagropromizdat, Moscow. Weiner, D.R., 2000. Models of Nature: Ecology, Conservation, and Cultural Revolution in Soviet Russia, 1st ed. ed, Russian and East European Studies. University of Pittsburgh Press, La Vergne. Yin, H., Prishchepov, A.V., Kuemmerle, T., Bleyhl, B., Buchner, J., Radeloff, V.C., 2018. Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series. Remote Sensing of Environment 210, 12–24. https://doi.org/10.1016/j.rse.2018.02.050 Additional Declarations No competing interests reported. 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Boxplots include the median (dot), 25\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e and 75\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e percentiles (boxes), and the first and last data point within two standard deviations (whiskers).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7753587/v1/92e7bc7b9acc7931d8fd4eeb.png"},{"id":94287449,"identity":"95e1ae8d-a24d-4cac-82ee-e099c0383657","added_by":"auto","created_at":"2025-10-27 11:05:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":118849,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEnergy flows per year in the 19\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e century. All flows measured in GJ.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7753587/v1/386670e889121d87d5997ac1.png"},{"id":94287629,"identity":"35eb6732-0c45-4e25-a237-61c41128c9f5","added_by":"auto","created_at":"2025-10-27 11:05:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":189082,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEnergy flows per year in the 21\u003c/em\u003e\u003csup\u003e\u003cem\u003est\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e century. All flows measured in GJ.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7753587/v1/f021ee348c8b7121f7ff3ba6.png"},{"id":94365053,"identity":"0b140758-be14-44ea-aa54-e808e2f8129c","added_by":"auto","created_at":"2025-10-27 13:08:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4004929,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7753587/v1/687c8c15-952e-44d2-bf70-16f9066f8205.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Peasant Subsistence to Industrial Farming: A Long-Term Analysis of Structural and Functional Changes in a Central Russian Agricultural Landscape","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Anthropocene is marked by unprecedented land system change, driven by population growth and agricultural expansion (Ellis et al., 2010). From 1700 to 2000, the global share of landscapes transformed by humans surged from 10% to 60% (Ellis et al., 2010), with profound socio-ecological consequences (Dang and Kawasaki, 2017; Foley et al., 2005; Klein Goldewijk et al., 2010). This transformation has led to significant ecological trade-offs, including biodiversity loss, soil degradation, and altered biogeochemical cycles. Once-natural ecosystems have been converted into complex human-nature coupled systems, where socio-economic and ecological processes interact at multiple scales (Bourgeron et al., 2018). Long-term socio-ecological research (LTSER) serves as a prominent framework for comprehending the interplay between environmental and societal dynamics over extended time scales, emphasizing the need to analyze cumulative impacts and develop sustainable land use policies (Angelstam et al., 2019; Dick et al., 2018; Singh et al., 2010). While LTSER has been widely applied in Western Europe (Fischer-Kowalski and Weisz, 2016; Gingrich and Krausmann, 2018), Russia’s unique agrarian history, marked by serfdom, collectivization, and post-Soviet privatization, which have left distinct legacies on the land, offers a distinct context to test these methodologies (Matasov et al., 2019; Moon, 2013; Prishchepov et al., 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTheoretical frameworks of LTSER emphasize the need to integrate historical data with modern analyses to uncover patterns and drivers of landscape change (Bodin and Tengö, 2012). Most studies rely on satellite imagery to track land changes over short periods of time (typically decades), missing long-term dynamics and making attribution of underlying drivers difficult (Plieninger et al., 2016; Singh et al., 2013). Such studies often employ supervized classification and transition matrices to quantify shifts in land cover, while statistical methods help identify drivers like soil fertility, proximity to settlements and infrastructure (Prishchepov et al., 2013; Yin et al., 2018). Quantifying long-term land use shifts requires integrating remote sensing data (e.g., Landsat) with historical cartography (Fuchs et al., 2015; Kaim et al., 2014). While these data provide indirect insights, they rarely delve into people's actual land use decisions (Gutman et al., 2020; Hersperger et al., 2010; Munroe et al., 2019). Alternative approaches, such as agent-based models, which attempt to simulate land use choices, require extensive data and are often confined to fine-scale studies with pre-defined actors (Valbuena et al., 2010). Reviews by Plieninger et al. (2016) highlight the importance of combining GIS-based cartographic analysis with ethnographic data to capture socio-economic influences. Existing research tends to focus on macro-level changes like agricultural expansion or urban growth, neglecting finer-scale changes in management intensity, such as fertilizer use, machinery deployment, labor inputs, and yield fluctuations (Aspinall and Staiano, 2019; Erb, 2012; Thomson et al., 2019). This limits our ability to fully capture land use intensification pathways (Erb, 2012; Thomson et al., 2019). Additionally, abrupt events like economic crises, policy shifts, or wars trigger rapid land use changes, while slow-onset processes like population growth cause gradual transformations (Kanianska et al., 2014; Scheffer et al., 2001).\u003c/p\u003e\n\u003cp\u003ePrevious research has identified typical land use patterns and their drivers (Jepsen et al., 2015; Levers et al., 2018), but these studies often overlook the internal functioning of agricultural systems, focusing instead on spatial patterns and management approaches (Erb, 2012). While spatial analyses effectively document land-cover transitions, they often overlook functional changes in agroecosystems, such as shifts in energy flows or nutrient cycling. A structural shift from peasant farming to industrial agriculture, for example, may be characterized as persistent cropland on a map, yet it masks a radical transformation in system metabolism, from biomass recycling to fossil-fuel dependency (Galán et al., 2016; Krausmann, 2004). Socio-metabolic theories (Haberl, 2015) and energy balance frameworks (Gingrich and Krausmann, 2018; Tello et al., 2016) address this gap by quantifying inputs (e.g., labor, fertilizers) and outputs (e.g., crop yields). Land system science increasingly distinguishes between \u003cem\u003estructural\u003c/em\u003e land-cover change (e.g., cropland expansion) and \u003cem\u003efunctional\u003c/em\u003e shifts in socio-metabolic flows (Marull et al., 2010; Meyfroidt, 2016).\u003c/p\u003e\n\u003cp\u003ePrevious works (Gingrich and Krausmann, 2018; Kuskova et al., 2008; Svirezhev et al., 1995; Tello et al., 2016) have established and further developed methodologies for reconstructing historical energy flows using agrarian statistics, but challenges persist. First, data scarcity, particularly for pre-industrial periods, often forces reliance on proxies (e.g., crop calorific values from analogous regions). Second, institutional changes (e.g., land reforms, political systems) can disrupt metabolic continuity or change the measurement units and statistical data collection system, complicating long-term comparisons, so data harmonization also becomes important (Aspinall and Staiano, 2019; Gingrich et al., 2016). Third, traditional energy balances in agriculture focus on external inputs and outputs, oversimplifying the complex internal dynamics of agroecosystems. Understanding intricate biophysical cycles, such as nutrient recycling and organic matter retention are crucial for grasping true energy dynamics. Thus, a more comprehensive approach is needed to accurately assess agricultural sustainability, incorporating multiple energy return on investment (EROI) metrics at different points within the system (Guzmán and González De Molina, 2015; Tello et al., 2016). Integrating spatial and metabolic analyses can provide a fuller picture of landscape-level sustainability in different regions or periods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe past 300 years have witnessed dramatic shifts in land use regimes across the globe, characterized by alternating phases of expansion, intensification, and abandonment (Jepsen et al., 2015; Ramankutty and Foley, 1999). In Russia, these transitions followed a distinct trajectory shaped by institutional shocks and socio-political upheavals. The pre-1861 serfdom era maintained a low-input, biomass-reliant land use regime, whereby peasant communities practiced extensive three-field rotations adapted to local environmental constraints. The abolition of serfdom initiated a gradual market integration phase, though land productivity remained limited by technological and climatic barriers (Alyabina et al., 2015). The Soviet period (1917–1991) marked a radical shift to a command-and-control regime, characterized by forced collectivization, mechanization, and input intensification, mirroring global trends but with unique ideological drivers (Jepsen et al., 2015; Weiner, 2000). Post-1991 reforms triggered a transitional regime of partial abandonment and agro-industrial consolidation, where market forces replaced central planning, yet legacy infrastructures persisted (Gutman and Radeloff, 2017; Prishchepov et al., 2012). Yet, Russia’s agroecological extremes (e.g., short growing seasons, low soil fertility in the northern territories) and spatial heterogeneity created land systems with distinct metabolic inefficiencies. Longitudinal analysis of these regimes underscores the need to integrate institutional, technological, and environmental drivers to explain nonlinear trajectories of change (Matasov et al., 2019).\u003c/p\u003e\n\u003cp\u003eHere, we aimed to assess functional and structural changes in Central Russian agricultural landscapes from the 19\u003csup\u003eth\u003c/sup\u003e to the 21\u003csup\u003est\u003c/sup\u003e centuries. To achieve it, we:\u003c/p\u003e\n\u003cp\u003e- compared the spatial structure of the landscape identified from historical maps (19\u003csup\u003eth\u003c/sup\u003e century) and satellite images (21\u003csup\u003est\u003c/sup\u003e century),\u003c/p\u003e\n\u003cp\u003e- assessed spatially explicit determinants of land-cover change, and\u003c/p\u003e\n\u003cp\u003e- reconstructed the energy flows using historical archives (19\u003csup\u003eth\u003c/sup\u003e century) and stakeholder surveys (21\u003csup\u003est\u003c/sup\u003e century).\u003c/p\u003e\n\u003cp\u003eBy integrating these approaches, we bridged LTSER’s spatial and metabolic paradigms, while focusing on an understudied, yet historically significant region.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e2.1. Study area and period\u003c/p\u003e\n\u003cp\u003eOur research was conducted within the projected territory of the P.P. Semenov-Tyan-Shansky Estate Museum \u0026ldquo;Ryazanka\u0026rdquo; (https://usadba-ryazanka.ru/), located on the border of Ryazan and Lipetsk regions, between the Central Russian Upland and the Oka-Don Plain (Fig. 1). The area belongs to the forest-steppe biome, characterized by fertile chernozem soils, moderate continental climate (mean January temperature: \u0026minus;10\u0026deg;C; July: +19.5\u0026deg;C), and annual precipitation of ~550 mm (Krivtsov, 2008). The vegetation primarily consists of grasslands, with oak forests scattered throughout. The landscape comprizes three main natural elements: interfluves with Dnieper-age moraine and loess-like loams, now mostly cultivated; erosion networks with gullies and ravines; and the Ranova river valley in the northern part of the study area (Matasov et al., 2018). Since the 18th century, the region has specialized in agriculture due to the relatively fertile soils and favorable climatic conditions.\u003c/p\u003e\n\u003cp\u003eThe study area is historically significant as the home of geographer Pyotr Semenov-Tyan-Shansky, who Semenov carried out a census of the local rural community and collected statistical data within his own manor and its vicinity (Semenov, 1880). These data, along with Semenov\u0026rsquo;s comments and conclusions, provide deep insights into the everyday life of Central Russian peasants in the 19\u003csup\u003eth\u003c/sup\u003e century. Our study uses the information from Semenov\u0026rsquo;s \u0026ldquo;Muraevenskaya volost\u0026rdquo; (republished in 2017) as the main source of historical statistical data (Semenov, 2017).\u003c/p\u003e\n\u003cp\u003eOur study area, encompassing about 11,500 ha, was a part of the Muraevenskaya and Pitelino volosts (administrative unit level 3) of the Ryazan province (level 1) in the 19\u003csup\u003eth\u003c/sup\u003e century. Today, this is the borderland between the Ryazan and Lipetsk regions. Around 10,000 people lived within the borders of the study area in the mid-19th century. Today, the population has decreased almost 10 times and amounts to 1181 people (Ryazanstat, 2017). Despite undergoing many changes related to administrative structures and settlement patterns over the last 200 years, these lands remain agriculturally active, which allowed us to compare pre-industrial and modern farming systems.\u003c/p\u003e\n\u003cp\u003eWe obtained information on land cover and land use, population and location-based characteristics (e.g. distances to roads and settlements) from several types of datasets: (a) historical land use records and topographic maps; (b) satellite imagery, including Sentinel-2 and Landsat-8 imagery from 2015 to 2020; (c) historical population and economic statistics as well as data from local agribusinesses that provided information on crop yields, machinery use, and fertilizer inputs; (d) a field-based landscape map at a scale of 1:20 000 (Matasov et al., 2018), characterizing the relief, vegetation and modern farming activities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2 Reconstruction of land use/land cover\u003c/p\u003e\n\u003cp\u003eTo analyse the historical land use, we relied on the 1:84 000 Mende Atlas (1850) from the Center for Historical Geography and Cartography (www.etomesto.ru). Each map contained boundaries and information about individual land holdings (\u0026ldquo;dachas\u0026rdquo;), descriptive information about land ownership and land use types as well as notes with supplementary economic information (Matasov et al., 2019; Milov, 1965). The Mende maps had a preliminary georeferencing, but they contained some deviations, thus, during the manual digitization of the land use types, some elements, such as ravines and villages, were corrected if needed based the guidelines for working with such materials (Kusov, 1993; Matasov V., 2016). We used visual interpretation of Landsat-8 and Sentinel-2 imagery jointly with very-high-resolution imagery available from the Bing and Google Earth Web Map Service layers after 2015 to obtain land cover for the year 2020.\u003c/p\u003e\n\u003cp\u003eSince the symbols on the Mende maps do not always correspond to the land types that exist today or the accepted terminology, a common legend was compiled for both time periods during digitization, enabling us to compare changes in land use patterns. Eleven land types were identified during the generalization process: arable land; dry meadows; floodplain meadows; fallow land; wet forests (black alder, willow, poplar nowadays, matched with sparse forest in the 19\u003csup\u003eth\u003c/sup\u003e century); dry forests (oak, birch, aspen nowadays; matched with dense forest in the 19\u003csup\u003eth\u003c/sup\u003e century); abandoned land \u0026nbsp;- former arable land covered in weeds and bushes; residential areas with small household gardens; water bodies; quarries and industrial areas (absent in the 19\u003csup\u003eth\u003c/sup\u003e century); land allocated for railways (absent in the 19\u003csup\u003eth\u003c/sup\u003e century).\u003c/p\u003e\n\u003cp\u003e2.3 Land use determinants\u003c/p\u003e\n\u003cp\u003eFor a 30-meter regular grid covering the entire study area, we recorded types of land use in 1882 and 2019 (see above), distances to roads and settlements (calculated from historical and current OSM data using GDAL). Distance to settlements can be used as an indicator of the attendance of each pixel, considering the necessity of delivering manure to the fields (Milov, 1965; Semenov, 2017). The changes in land use relationship with settlement structure and road network were analyzed using R (Development Core R Team, 2011)\u003c/p\u003e\n\u003cp\u003e2.4 Energy flow analysis\u003c/p\u003e\n\u003cp\u003eWe adapted the Energy Return on Investment (EROI) framework\u0026nbsp;(Tello et al., 2016) to compare agroecosystem efficiency in two periods: the middle 19\u003csup\u003eth\u003c/sup\u003e century subsistence system and the modern industrialized system. Firstly, we determined the energy inputs and outputs in both periods. We used historical data on the main crops cultivated, their yields and the areas of arable land occupied by them, the number of livestock, the number of people employed in agriculture, and the ratio of areas under different types of land use (Semenov, 2017). Information on the current yield of each agricultural machinery and the number of workers was obtained from the agricultural company\u0026rsquo;s administration upon request. The local population size and number of animals kept on private farms were obtained from the municipal administration.\u003c/p\u003e\n\u003cp\u003eThe crop production in the 19\u003csup\u003eth\u003c/sup\u003e century was calculated using data on the yield of rye and oats, their share of arable land, and field area (Semenov, 2017). Approximately 1/3 of the harvest went to livestock feed (Biomass Reused), the rest was exported outside the system boundaries. Another type of Biomass Reused - agricultural waste (straw) - was estimated at a 1:1 ratio to the harvest (Tello et al., 2016).\u003c/p\u003e\n\u003cp\u003eWhen calculating livestock production, we chose cow milk, pork and lamb production only for comparability with modern data. Several assumptions were made: all cattle were assumed to be dairy cows due to lack of precise historical data; all sheep and pigs were counted toward meat production (assumed to be kept no longer than a year).\u003c/p\u003e\n\u003cp\u003eLivestock waste (manure) was calculated based on the average daily excrement per animal (Kuzmin, 2012), accounting for dry matter content (Vasilev and Filippova, 1988). To estimate the livestock feed we used the annual demand of each livestock species in MJ. One third of feed came from arable land, the rest - from pastures and hayfield, considering land area, yield, and energy value of feed (Bulatov et al., 2016; Samojlov and Sechin, 2017).\u003c/p\u003e\n\u003cp\u003eIn the 19th century, labor energy included the power of horses and human labor. It was calculated based on the annual feed requirements for work and daily bread rations, as well as the size of the working population (Milov, 1965). Today such information is based on the human daily energy requirement (Kuzmin, 2012; Tello et al., 2016) and the number of employees of the agricultural holding, proportional to land area. The energy flow from the machinery utilized in modern times was estimated based on the number of machines and their annual working hours (Aguilera et al., 2015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMineral fertilizers also represent an energy input, and were calculated using the company\u0026rsquo;s data on the volume of each type of fertilizer, proportional to the area of arable land in the key area.\u003c/p\u003e\n\u003cp\u003eFinally, all flows (tons of crops/fertilizers, work hours, food calories, etc.) were converted to energy units. In the methodology proposed by Tello et al. (2016), such a unit is the gross calorific value (GCV), which shows how much heat is released during the complete combustion of 1 kg of a substance. However, for many flows, particularly those denoted through the amount of food or feed, nutritional energy value - the estimated amount of heat energy produced by a living organism when digesting the food eaten - can be applied. In our study the human/animal labor and feed were calculated via nutritional energy (kcal or MJ), all other flows (crops, waste, etc.) calculated via GCV (MJ/kg). In the table below we summarized all flows and units.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1. Methodology and sources for energy flows estimation.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"589\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlow Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource of estimation methodology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eCrops, straw\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(Guzm\u0026aacute;n and Gonz\u0026aacute;lez De Molina, 2015; Milov, 1965)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eMJ/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eManure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(Sahu et al., 2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003ekcal/kg \u0026rarr;MJ/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eFertilizers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(Aguilera et al., 2015; Vasilev and Filippova, 1988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eMJ/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eMachinery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(Aguilera et al., 2015; Kuzmin, 2012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eMJ/h\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eHuman/animal labor and feed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e(Kuzmin, 2012; Milov, 1965)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003ekcal/MJ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy converting all flows of the agroecosystem into joules, we could analyze its functioning. Solar energy, being essential for crop growth, biodiversity, and other processes, was excluded from these energy flow calculations, because it is a constant. The energy balance was always different from zero, as the analyzed agroecosystems are not closed: it receives inputs (fertilizers, feed, machinery) and exports outputs (crop and livestock products). In the 19\u003csup\u003eth\u003c/sup\u003e century, internal flows included crop residues used as livestock bedding and animal waste recycled as organic fertilizer. Today, no such closed cycles exist and all waste leaves the system. Energy inputs now come entirely from external sources, independent of the agroecosystem\u0026rsquo;s internal mechanisms, while outputs are determined by societal demand.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThus, to assess energy efficiency of the system, we calculated the following indices:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eEFEROI (External Final Energy Return on Investment) = final output / \u0026Sigma; external inputs\u003c/li\u003e\n \u003cli\u003eIFEROI (Internal Final Energy Return on Investment) = final output / reused biomass\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFEROI (Final Energy Return on Investment) = final output / reused biomass + \u0026Sigma; external inputs.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1. Changes in Landscape Structure (1860\u0026ndash;2020)\u003c/p\u003e\n\u003cp\u003e3.1.1 Land use structure in the19th century\u003c/p\u003e\n\u003cp\u003eAt the end of the 19\u003csup\u003eth\u003c/sup\u003e century (1860-1880), the study area contained 14 settlements and 20 agrarian communes. Single-estate villages formed one commune, while multi-estate settlements had multiple communes based on differing land charters (reflecting varying peasant emancipation statuses). Most residents were serfs under corv\u0026eacute;e labor. Eight landed estates existed, with land owned either by nobles or communes. Communal land ownership emerged after abolition of serfdom, though 14 of 20 communes still experienced some serfdom practices. Peasants were managed by landowners or stewards, typically receiving small, irregularly-shaped plots adjacent to ravines. Semenov classified most households as marginal-to-poor (Semenov, 2017).\u003c/p\u003e\n\u003cp\u003eThe economy of Muraevenskaya volost was fundamentally agricultural, employing the traditional three-field system which provided both employment and subsistence for most residents. The rotation comprized: winter fields: rye (80-85%) with some winter wheat (15-20%); spring fields: predominantly oats (75-80%), supplemented by buckwheat (5-10%), millet (10%) and flax; and fallow field. Manure served as the sole fertilizer.\u003c/p\u003e\n\u003cp\u003eBeyond agriculture, peasants engaged in supplementary industries: peat extraction, coal mining, brick/lime production, stonemasonry, metalworking, construction trades, flour milling, beekeeping, poultry farming, leatherworking, textiles, shoemaking, dyeing, and seasonal migrant labor. Agricultural income from allotted and private lands proved insufficient for redemption payments and taxes, necessitating these off-farm earnings (Semenov, 2017).\u003c/p\u003e\n\u003cp\u003eMost of the territory (~8 thousand ha) was occupied by arable land (Fig. 2, top). 7% of the area (~1 thousand ha) was covered in forest. In economic notes of the 19\u003csup\u003eth\u003c/sup\u003e century there were two types of forests differentiated not by the species but by possible use \u0026ndash; for fuel (410 ha) and for constructions (570 ha). First one mostly located in wet valleys, and second one \u0026ndash; on dry slopes of interfluves. The dry meadows were predominantly situated along gullies and ravines (1180 ha). Wet meadows and marshes occurred throughout the Ranova River valley (177 ha). Meadows were predominantly used for hay harvesting and grazing. Settlements with orchards and allotments occupied 515 ha. Beyond the river itself, water bodies included seasonal ponds in gully headwaters. Fields near Urusovo on Ranova\u0026apos;s left bank lay fallow, while waterlogged areas surrounding villages remained uncultivated.\u003c/p\u003e\n\u003cp\u003e3.1.2. Land use structure in the 21st century\u003c/p\u003e\n\u003cp\u003eToday, the territory comprizes two rural groups of settlements (similar to volost): Urusovsky (8 villages) and Miloslavskoye (19 villages). While most settlements have retained their 19\u003csup\u003eth\u003c/sup\u003e century names, new ones have emerged, such as Yuzhny, founded as an administrative center during Soviet times.\u003c/p\u003e\n\u003cp\u003eThe area\u0026apos;s primary land use remains largely unchanged \u0026ndash; it persists as an agricultural landscape dominated by crop cultivation. The interfluve lands are under arable farming, where an agricultural holding cultivates winter and spring crops (wheat, barley, rapeseed) using a multi-field crop rotation system and their own machinery servicing several districts of Lipetsk region. Local residents are scarcely involved in agricultural operations, unlike 19\u003csup\u003eth\u003c/sup\u003e century peasants. Their activities are limited to small-scale subsistence farming (poultry, pigs), small gardens near homes, and shared vegetable plots on village outskirts. Livestock grazing occurs informally on abandoned lands near settlements.\u003c/p\u003e\n\u003cp\u003eThe current land use structure has become more fragmented, while maintaining its fundamental patterns (Fig. 2, bottom). Watershed areas remain dominated by arable land (6645 ha), incorporating many erosion features. Dry meadows (1732 ha) still occupy slopes and bottoms of gullies, though their area has expanded due to gully growth. The upper sections of gullies have become overgrown with oak-birch forests, reducing arable land. Water bodies in catchment depressions have disappeared due to field levelling works. Floodplain meadows have given way to wet alder-willow woodlands. Abandoned lands overgrown with weeds and shrubs have increased significantly, particularly on abandoned arable land. Fallow fields have been largely replaced by completely abandoned lands, while residential areas have shrunk, with many villages vanishing entirely, replaced by forests and shrubs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOver the two centuries, the landscape underwent significant changes, yet the overall structure of land use remained largely intact (Table 1). Agriculture persists as the primary land use, with arable land still covering the largest area. However, the extent of cultivated land decreased by 19%, particularly in slopes near ravines and valleys\u0026mdash;now replaced by dry forests and dry meadows (which expanded by 92% and 46%, respectively). Unused land also increased due to abandoned pastures and floodplain hayfields (down by 16%). With population decline, residential land use saw a sharp reduction (-71%). Former village sites are now predominantly abandoned land (+60%). Drainage of waterlogged depressions in upper ravine areas has reduced water body area by 75%. There has been a significant increase in fallow land (+181%) and floodplain forests (+94%) due to discontinued haymaking.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1. Change in land area from 1860 to 2020\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"580\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLand Use Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea in 1860 (ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea in 2020 (ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1. Arable land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8196,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6645,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026minus;19%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2. Dry meadows\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1183,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1732,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+46%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3. Floodplain meadows\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e177,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e150,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026minus;16%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4. Fallow land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e179,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e506,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+181%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5. Wet/sparse forests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e411,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e799,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6. Dry/dense forests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e571,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1098,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+92%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7. Abandoned land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e236,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e376,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e+60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8. Gardens \u0026amp; residential land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e515,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e149,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026minus;71%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9. Water bodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026minus;75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10. Quarries \u0026amp; excavations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11. Railway land take\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.2. Settlement and land use change\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe land use structure of the study area underwent significant reorganization between the 1860s and the 2020s, driven by a fundamental shift in the socio-economic and technological determinants of agricultural production. In the 19\u003csup\u003eth\u003c/sup\u003e century, the spatial organization of land was a function of the peasant subsistence economy, which relied on human and animal labor. This created a landscape where proximity to settlements was the paramount factor. The need for farmers to walk to their fields daily resulted in a relatively uniform distribution of settlements across the study area, with some of them scattered across the interfluves (Fig. 2). Villages with adjacent household gardens were surrounded by intensively manured arable land and, where possible, floodplain meadows (Fig. 3a). The dense network of unpaved roads served to connect settlements to this radiating pattern of fields and meadows, with around three quarters of arable land lying within 500 m of a path (Fig. 3b). The road network likewise provided access to the forests, resulting in a fragmented natural landscape, yet a well-connected rural community.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy the beginning of the 21\u003csup\u003est\u003c/sup\u003e century there was a significant decoupling of land use from human settlement patterns. Mechanization of agriculture abolished the walking distance constraint. Consequently, the influence of proximity to settlements diminished, resulting in abandonment of the majority of the interfluve, water-deficient villages. Arable fields and meadows are now further from the villages (Fig. 3a), whereas much of the area adjacent to remaining villages is either unused land, or floodplain forests providing recreational services. An even more significant change can be observed in the road network (Fig. 3b), with road to field median distance increasing more than twice, reflecting the use of heavy equipment that can traverse longer distances. Today, the most extensive and productive croplands are concentrated in sparsely populated interfluvial plateaus, operated by workers who commute rather than reside nearby.\u003c/p\u003e\n\u003cp\u003eIn summary, the land-use system transitioned from a settlement-centric, labor-intensive model to an infrastructure-dependent, capital-intensive model. This has led to a more fragmented landscape where the highest-productivity lands are maximized for machine-based cultivation, while less accessible or fertile areas are abandoned to ecological succession.\u003c/p\u003e\n\u003cp\u003e3.3. Energy Flow Dynamics\u003c/p\u003e\n\u003cp\u003e3.3.1. Energy indicators of 19\u003csup\u003eth\u003c/sup\u003e century agroecosystem (closed-loop system) functioning\u003c/p\u003e\n\u003cp\u003eWithin the agroecosystem of Muraevenskaya volost, three blocks can be distinguished: the crop production block, which includes arable land; the livestock production block, which includes livestock as well as hayfields and pastures indirectly; and the forestry block. This paper focuses on the first two blocks because there is insufficient data on forestry production. Labor was provided by peasants living in the villages of the volost. The hayfield and pasture subsystems mainly interacted with livestock farming: pastures provided fodder for livestock in summer and hayfields in winter.\u003c/p\u003e\n\u003cp\u003eThe livestock was dominated by sheep (4205 heads), cows (1424) and pigs (398). Horses (1428) made up the main labor force. However, insufficient hayfields and pastures hindered the development of cattle breeding. The crop production area within the study area boundaries covered 8196 hectares. The entire arable area was divided into three parts due to the adopted three-field system. The oat yield was about 2.7 c/ha and the rye yield - 7.8 c/ha.\u003c/p\u003e\n\u003cp\u003eThe energy flow diagram (Fig. 4) illustrates the energy loops between the agroecosystem (subsystems of crop production and animal husbandry) and society. The largest energy flow comes from the crop production system, as represented by the rye and oat crops. This accounts for 27,557 GJ per year, which is harvested from the largest area within the agroecosystem. Some of this energy is converted into plant biomass when it is fed to livestock and consumed by people. The corresponding arrows move away from crop production. According to the scheme proposed by Tello et al. (2016), people employed in the agroecosystem are not part of it. This means that the energy from crop production that feeds them first leaves the boundaries of the agroecosystem and only then reaches the peasants. Therefore, the value of labor energy spent by peasants on agricultural work is not subtracted from crop production because the said production first goes outside the system boundaries. The livestock feed takes 12560 GJ from the harvested crop, i.e. a little less than half of the total energy of this flow. This is one third of the total annual energy that animals require. After deducting the output going to the livestock subsystem, the remaining crop production is equivalent to 14,997 GJ. This is a significant amount of crop production, as confirmed by the descriptions in \u0026apos;Muraevenskaya volost\u0026apos;: \u0026ldquo;\u003cem\u003e...in general, the amount of rye produced by peasant lands gave a surplus against the need\u003c/em\u003e\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eA significant aspect of crop production was the generation of waste, with straw being used as animal bedding. With a ratio of 1:1 (Guzm\u0026aacute;n and Gonz\u0026aacute;lez De Molina, 2015; Tello et al., 2016), the volume of waste (34,420 GJ) equaled that of the crop. The energy value of straw was higher than that of rye and oats due to its higher calorific value. This waste represented reused biomass remaining within the agroecosystem, thereby increasing its sustainability and reducing its dependence on external energy sources.\u003c/p\u003e\n\u003cp\u003eThe livestock subsystem received energy from arable land, hayfields and pastures. Hayfields (with herb and reed grass varieties) provided 56,325.7 GJ per year and their produce was stored for the winter months. Pastures (grain and legume-grass varieties) provided 32,861.6 GJ during the summer. The total energy output of livestock production (milk, pork and lamb) was 17,163.5 GJ. Manure, a waste product of livestock production, was distributed to crop production and associated biodiversity in equal flows of 73,018 GJ. In summer, livestock grazed on pastures and the manure remained there, fertilizing the soil and replenishing the biodiversity subsystem. In winter, it was collected, dried and transported to arable land. As Semenov-Tyan-Shansky noted, \u0026ldquo;\u003cem\u003epeasants take all the manure to the arable land without a rest\u003c/em\u003e\u0026rdquo; (Semenov, 2017).\u003c/p\u003e\n\u003cp\u003eHorses were a part of the livestock subsystem and served as the main labor force. Although their labor was formally directed towards crop production, it was expressed energetically through the feed they consumed, which was estimated at 84,109 GJ. Peasants involved in agriculture spent 4,598 GJ on their work per year.\u003c/p\u003e\n\u003cp\u003eThe total output of crop and livestock production is 32160.5 GJ. The predominance of crop production over livestock breeding is due, firstly, to the larger areas of arable land and, secondly, to the orientation of peasant farms of that time to crop production due to the availability of fertile soils.\u003c/p\u003e\n\u003cp\u003eBy analyzing energy flows, we calculated several energy efficiency metrics. External Final Energy Return on Investment (EFEROI) amounted to 6.99, meaning that the system outputted nearly 7 times more energy than it received from external inputs. The agroecosystem acted as a net energy donor to society\u0026mdash;its production (primarily crop-based) was consumed externally, while energy inputs were minimal (mainly human labor, which requires relatively low energy investment). Internal Final EROI (IFEROI) was 0.68. The reused biomass (crop residues like straw and fodder) amounted to 49,980 GJ, compared to the final output of 32,160.5 GJ. This internal recycling reduced reliance on external resources, enhancing system resilience. Final EROI (FEROI) was 0.62.\u0026nbsp;Since the final output is less than the sum of reused biomass and external inputs, this indicates efficient agroecosystem functioning. By maximizing internal resource cycling and minimizing external dependence, the system remains energy-sustainable while supplying surplus energy to society.\u003c/p\u003e\n\u003cp\u003e3.3.2. Modern Agroecosystem (Industrialized System)\u003c/p\u003e\n\u003cp\u003eToday, the main subsystem is crop production, with crops being cultivated, harvested, and sold by the agricultural holding. The livestock subsystem, according to the administration\u0026rsquo;s comments, is represented by small-scale private farms of the local population and is incomparably smaller than crop production (Fig. 5). There are 63 units of cows, 147 sheep, 58 pigs, 1,850 poultry, 106 bee colonies in the private farms. Unlike the 19th century, when the main workforce consisted of local residents, today this contribution comes from outside through the agricultural holding, whose employees are partially engaged in the lands of the rural settlement. The local population (1181 people), however, does not participate in the agricultural holding\u0026rsquo;s farming activities and consumes all livestock products from their private farms themselves rather than selling them.\u003c/p\u003e\n\u003cp\u003eIn 2018, the agricultural holding harvested 5,116 tons of winter wheat, 3,568 tons of summer wheat, 5,576 tons of barley, and 3,132 tons of sunflower. It is worth noting that the crops grown on the fields vary from year to year. For example, in 2019, most fields were sown with rapeseed, spring barley, and spring wheat. The three-field system, which was common in the 19\u003csup\u003eth\u003c/sup\u003e century, has been replaced by a more diverse multi-field approach.\u003c/p\u003e\n\u003cp\u003eThe configuration of the subsystems has also changed: the agroecosystem is now interacting with both villagers living within the study area and the agricultural holding as the main agent of agricultural production. The agricultural holding receives external inputs, fertilizers and machinery, directed toward crop production, which it manages. However, there is no connection between the agricultural holding and residents or private subsidiary farms (the equivalent of the livestock subsystem). Thus, the system has become open. The agricultural holding\u0026apos;s crop (153,535 GJ) is completely exported from the fields without losses. Crop production waste (13,266 GJ) - crushed straw - left on the fields to decompose, becoming a variety of associated biodiversity. A high volume of crop production is provided by significant energy costs. In the modern agroecosystem, two energy flows have appeared that were absent in the time of Semenov-Tyan-Shansky: mineral fertilizers (1,422,117.2 GJ) and agricultural machinery (81,298.7 GJ). Machinery has replaced horses, and fertilizers have replaced organic matter. The energy of fertilizers is almost 100 times higher than the output. Human labor (333 GJ/year) is now represented by employees of the agricultural holding.\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;local residents \u0026ndash; private farms\u0026quot; subsystem includes two energy flows: the purchase of feed for livestock (4,488 GJ) and the receipt of livestock products (1,390 GJ \u0026ndash; milk, lamb, pork). The feed is supplied mostly externally, due to small backyards for growing on household plots, which reduces the energy efficiency of the system itself. Although the analysis includes only basic products for comparison with the 19th century, agricultural enterprises also produce eggs (43 GJ), chicken (15 GJ), beef (39 GJ), goat\u0026apos;s milk (298 GJ) and honey (32 GJ). However, even with these products, energy consumption exceeds the return, making the system energy unprofitable.\u003c/p\u003e\n\u003cp\u003eIn addition to products, animals also produce manure, which enters the associated biodiversity as livestock waste, since mineral fertilizers from the agricultural holding are used on the arable land. Local residents also contribute to this waste flow from their subsidiary farms, thus jointly this flow estimated at 11742 GJ.\u003c/p\u003e\n\u003cp\u003eExternal Final Energy Return on Investment (EFEROI) amounted to 0.10.External energy inputs vastly exceed output (crop production only) value is 70 times less efficient than in 1860s. EFEROI equals total Final EROI (FEROI). The system lacks biomass reuse, as waste is disposed rather than recycled, making internal return (IFEROI) incalculable. Modern agroecosystem consumes substantially more energy than it produces.\u003c/p\u003e\n\u003cp\u003e3.3.3 Factors of changes in the functioning and energy efficiency of the agroecosystem\u003c/p\u003e\n\u003cp\u003eOver two centuries, the general appearance of the study area has changed insignificantly, but its functioning has drastically transformed. The livestock and crop farming subsystems that existed in the 19th century have evolved. Traditional livestock farming has disappeared, replaced by small private household plots not aimed at full self-sufficiency. Crop farming has largely retained its previous form, but now both subsystems interact with associated biodiversity, whereas in the past, only livestock waste entered the ecosystem.\u003c/p\u003e\n\u003cp\u003eSome elements of the agroecosystem, such as hayfields and pastures, have vanished. Although meadows (wet and dry) remain, they are no longer used for grazing. The human role has also changed: while peasants once worked the land and consumed its produce, arable land is now controlled by an agricultural holding that owns vast areas. As a result, human presence in the agroecosystem is limited to two separate groups: local rural residents and the agricultural holding, which coexist without intersecting.\u003c/p\u003e\n\u003cp\u003eChanges in energy flows reflect shifts in the relationships between subsystems (table 2). For example, today, livestock farming and crop production are no longer interconnected, whereas in the 19th century they exchanged multiple flows: crop production provided fodder and straw, while livestock farming supplied organic fertilizers and the labor of horses for work in the fields. Another example of changing relationships is the shift in animal feed sources, from internal subsystems (arable land, pastures, hayfields) to external ones (as local residents now mostly purchase feed rather than extracting it from the agroecosystem).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2. Main indicators of changes in land system functioning\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003csup\u003eth\u003c/sup\u003e century\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003csup\u003est\u003c/sup\u003e century\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of livestock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1424 cattle\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4205 sheep\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1428 horses\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e398 pigs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63 cattle\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e147 sheep\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e58 pigs\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1850 poultry\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e106 bee colonies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFinal production, GJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e153 535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCrop production, GJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e153 535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLivestock production, GJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal external inputs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 503 748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReused biomass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46 980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLivestock waste\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e146 036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCrop waste\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34 420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLivestock services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84 109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLivestock feed inputs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e101 747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal livestock inputs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e136 167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal crop inputs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e161 725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 503 749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEFEROI (final production/external inputs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIFEROI (final production /biomass reused)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFEROI (final production/biomass reused + external inputs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWhen discussing changes in the flows themselves, it is important to note the transition in external inputs from relying solely on peasant labor to the energy of fertilizers and agricultural machinery. Notably, total external inputs today are more than 300 times greater than in the 19th century.\u0026nbsp;The main change in the amount of energy flows is due to the transformation of economic mechanisms: modern agriculture is increasingly dependent on external resources such as fertilizers and machinery, in contrast to traditional factors such as the use of horses and local labor. This has led to a significant increase in energy consumption in the former compared to the latter.\u003c/p\u003e\n\u003cp\u003eThe energy efficiency indicators of the agroecosystem have decreased. The external final energy return (EFEROI) dropped from 7 to 0.1 due to an imbalance between energy input and output: the modern system consumes more than it produces, making it less efficient than in the 19\u003csup\u003eth\u003c/sup\u003e century.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe observed decline in energy efficiency of agricultural systems in the Muraevenskaya volost reflects a global trend yet demonstrates distinctive features characterizing Russia's agroecological and socioeconomic context. Our analysis reveals that while the EFEROI index decreased from 6.99 to 0.10 over the study period - mirroring patterns documented in Western Europe (Galán et al., 2016; Tello et al., 2016) - the Russian case was marked by significantly lower baseline productivity. Comparing our energy efficiency results with literature data from Russia and eslewhere, we see a general trend: a decrease in energy efficiency with an increase in production volumes. For example, the agroecosystem of Catalonia (Tello et al., 2016) is similar to ours in area, but due to a milder climate and a long growing season, it produced 3 times more energy in the 19\u003csup\u003eth\u003c/sup\u003e century (EFEROI = 21.53) than it was invested in. However, its dependence on external resources was higher (IFEROI = 1.08). By 21\u003csup\u003est\u003c/sup\u003e century, the indicators had decreased both there and in Russia: EFEROI — to 0.25, IFEROI — to 2.20, FEROI — to 0.22. According to (Fuzella, 2009), the EFEROI for the Tomsk Region in Siberia is even lower, at 0.14. This confirms that modern agricultural systems require more energy than they return, which is consistent with the theory of increasing system complexity and energy consumption (Marull et al., 2010; Puzachenko et al., 2011). This phenomenon can be attributed to the complex interplay of severe climatic constraints, including short growing seasons that limited biomass accumulation, and institutional barriers such as serfdom's land management practices. The climatic conditions of the forest-steppe zone, particularly cold winters that slowed natural decomposition processes, created a unique \"low-productivity trap\" that continues to influence modern agricultural systems in the region (Lyuri et al., 2010). These findings suggest that climate-institutional interactions have played a more significant role in shaping Russian agricultural efficiency than previously recognized, with implications for understanding similar frontier regions globally.\u003c/p\u003e\n\u003cp\u003eThe limitations of relying solely on structural land-cover analysis become particularly apparent when examining the Muraevenskaya case. While traditional metrics might suggest relative stability through the 19% reduction in cropland area, our energy flow analysis reveals fundamental transformations in system functioning. The peasant agricultural system of the 1860s maintained tight nutrient cycles within local area, recycling 46,980 GJ/year of biomass, whereas contemporary systems depend overwhelmingly on external fossil fuel inputs totaling 1,503,748 GJ/year. This metabolic transition occurred despite superficial continuity in land cover patterns, underscoring the importance of complementing spatial analyses with functional assessments (Haberl, 2015). The discrepancy between spatial proxies and actual system functioning highlights the potential for misinterpretation when relying exclusively on land cover change data, particularly in regions undergoing complex socioeconomic transitions. Our findings align with growing recognition in land system science that intensification pathways must be evaluated through both spatial and metabolic lenses to fully understand sustainability trade-offs.\u003c/p\u003e\n\u003cp\u003eSeveral critical knowledge gaps emerged from our analysis, particularly regarding the role of forest ecosystems and grassland productivity in historical agricultural systems (Beug, 1967; Krausmann et al., 2012; Moon, 2013). Based on rural economy descriptions (Semenov, 2017) we can suggest that woodland resources can contribute up to 15-20% of total energy inputs during the peasant period, comparable to patterns observed in the Alps (Bolliger et al., 2017; Gingrich and Krausmann, 2018), yet detailed reconstruction of “forestry metabolism” remains challenging due to data limitations. Similarly, uncertainties persist in quantifying historical grassland productivity, particularly in interpreting traditional hay yield measures such as the \"kopna\" unit, which could vary two- to threefold depending on local conditions (Milov, 1965). These knowledge gaps point to the need for innovative methodological approaches combining ethnographic research with paleoecological proxies to better reconstruct historical land use practices (Novenko et al., 2017; Poska et al., 2014; Seddon et al., 2014). Future studies in comparable regions could benefit from incorporating dendrochronological data and soil charcoal analysis to complement archival records, providing a more comprehensive understanding of long-term ecosystem dynamics.\u003c/p\u003e\n\u003cp\u003eThe institutional evolution of land management in the Muraevenskaya volost reveals path dependencies with important implications for sustainable transitions. From the constraints of serfdom through the collectivization period to contemporary agribusiness dominance, Russian agriculture has consistently prioritized production over circularity, resulting in persistent neglect of biomass recycling principles. This trajectory lays along with Western European experiences where institutional frameworks start to discuss sustainability considerations after wide ecological negative impacts (Pe’er et al., 2020; Stoate et al., 2009). Potential pathways forward could draw on historical adaptation strategies while incorporating modern technologies, such as combining elements of traditional multifunctional land use and precision agriculture techniques.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings challenge conventional intensification narratives by demonstrating that yield increases achieved through metabolic simplification may come at unacceptable sustainability costs (Biggs et al., 2015; Thomson et al., 2019). The Muraevenskaya case study suggests that truly sustainable agricultural systems require metabolic transparency across entire production chains, from field to fork, combined with institutional frameworks that value circularity. As Russia faces increasing climate variability and global market fluctuations, the lessons from this 150-year trajectory gain urgency. Future research should focus on developing regionally adapted models that reconcile productivity goals with energy efficiency, drawing on both traditional knowledge and technological innovations (Marull et al., 2010). Such approaches will be critical not only for Russia's agricultural future but for similar grain-exporting regions worldwide facing comparable sustainability challenges.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003col\u003e\n\u003cli\u003e\u003cem\u003eMetabolic Over Structural Change:\u003c/em\u003e The most significant transformation from the 19\u003csup\u003eth\u003c/sup\u003e to the 21\u003csup\u003est\u003c/sup\u003e century was not the change in land cover, which remained relatively stable, but the fundamental shift in the system's socio-metabolic functioning.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eEfficiency-Return Trade-off:\u003c/em\u003e The transition from a biomass-based peasant system to a fossil-fuel-driven industrial regime resulted in a drastic thousand-fold decline in energy efficiency (EFEROI fell from 6.99 to 0.10), despite an increase in absolute production output.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eDecoupling from Local Constraints:\u003c/em\u003e A primary driver of change was the decoupling of agricultural production from local socio-ecological factors. Mechanization and depopulation eliminated the constraint of walking distance, replacing it with a dependency on external inputs and road infrastructure.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eLoss of Circularity:\u003c/em\u003e The modern agroecosystem is characterized by broken internal cycles. The critical flows of biomass reuse and organic fertilization that sustained the historic system have been replaced by linear inputs of fossil energy and minerals.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003ePolicy Implications:\u003c/em\u003e Sustainability assessments cannot rely solely on spatial land cover data but must integrate functional metabolic analysis. Policies aiming for sustainable agricultural futures should prioritize reintegrating circular economy principles and reducing dependence on external inputs, rather than pursuing further intensification through non-renewable resources.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Bogdanov A.A., researcher at the Semenov-Tyan-Shansky Museum, for providing archival information and assistance in organizing field research, including interviews with an agricultural company and a municipal administration. We would also like to express our gratitude to our colleagues Juan Marull, Roc Padro, and Enric Tello for their advice on calculating the energy balance and other aspects of the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese studies were supported by a grant from the Ministry of Science and Higher Education of the Russian Federation (agreement NO. 075-15-2024-554 of 24 April 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, V.M.; methodology, V.M. and M.K.; software, N.S. and O.Z.; formal analysis, M.K., N.S. and O.Z.; investigation, M.K., O.Z. and V.M.; resources, M.K.; data curation, M.K., N.S.; writing—original draft preparation, V.M.; writing—review and editing, V.M., M.K. and O.Z.; visualization, V.M. and M.K.; supervision, V.M.; project administration, V.M.; funding acquisition, V.M. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAguilera, E., Guzm\u0026aacute;n, G.I., Infante-Amate, J., Garc\u0026iacute;a-Ruiz, R., Herrera, A., Villa, I., Carranza, G., de Molina, M.G., 2015. EMBODIED ENERGY IN AGRICULTURAL INPUTS. INCORPORATING A HISTORICAL PERSPECTIVE.\u003c/li\u003e\n\u003cli\u003eAlyabina, I.O., Golubinsky, A.A., Kirillova, V.A., Khitrov, D.A., 2015. Soil resources and agriculture in the center of European Russia at the end of the 18th century. 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Models of Nature: Ecology, Conservation, and Cultural Revolution in Soviet Russia, 1st ed. ed, Russian and East European Studies. University of Pittsburgh Press, La Vergne.\u003c/li\u003e\n\u003cli\u003eYin, H., Prishchepov, A.V., Kuemmerle, T., Bleyhl, B., Buchner, J., Radeloff, V.C., 2018. Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series. Remote Sensing of Environment 210, 12\u0026ndash;24. https://doi.org/10.1016/j.rse.2018.02.050\u003c/li\u003e\n\u003cli\u003e\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"energy-ecology-and-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eeae","sideBox":"Learn more about [Energy, Ecology and Environment](http://link.springer.com/journal/40974)","snPcode":"40974","submissionUrl":"https://submission.nature.com/new-submission/40974/3","title":"Energy, Ecology and Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"socio-ecological systems, agricultural landscape, land system functioning, EROI, energy flows, long-term dynamics, land use change","lastPublishedDoi":"10.21203/rs.3.rs-7753587/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7753587/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Anthropocene is characterized by profound land system changes, yet many studies focus solely on structural land cover transitions, overlooking the critical functional shifts within socio-ecological systems. Our study bridges this gap by integrating spatial analysis and socio-metabolic theory to examine the long-term evolution of an agricultural landscape in the forest-steppe of Central Russia from the mid-19th century to the present time. Using historical maps, satellite imagery, spatial statistics, and energy flow analysis based on historical archives and modern surveys, we quantify both the structural land use changes and the fundamental transformation of the land system's metabolism.\u003c/p\u003e\n\u003cp\u003eOur results reveal a landscape of structural persistence, with the total area of arable land decreasing by only 19% over 160 years. However, this stability masks a radical metabolic shift. The mid-19th century biomass-based subsistence system was a closed-loop one, characterized by high energy efficiency, internal biomass recycling (46,980 GJ/year), and a land use pattern tightly coupled to settlement locations and human labor. In stark contrast, the modern industrialized system is entirely dependent on massive external fossil fuel inputs (1,503,748 GJ/year), has severed internal energy cycles, and exhibits a drastically reduced energy return on investment. Agricultural land use has decoupled from settlements and is now primarily determined by proximity to road infrastructure for machinery access.\u003c/p\u003e\n\u003cp\u003eWe conclude that the transition from the subsistence system to the industrialized one, driven by institutional and technological changes, has led to a severe decline in metabolic efficiency, despite increased yields. Our study underscores the critical importance of complementing spatial analysis with functional energy flow assessments to fully understand the sustainability of land systems and their long-term trajectories.\u003c/p\u003e","manuscriptTitle":"From Peasant Subsistence to Industrial Farming: A Long-Term Analysis of Structural and Functional Changes in a Central Russian Agricultural Landscape","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-24 09:26:14","doi":"10.21203/rs.3.rs-7753587/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-19T11:52:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-19T11:31:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-19T09:38:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140758174692562395642214112978663897761","date":"2026-01-02T10:29:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148632470718595233566631791046960009648","date":"2025-12-31T16:07:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"83961965995727972279462404135282804487","date":"2025-12-29T11:47:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18346548669627896929393017574249385433","date":"2025-12-29T06:24:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-24T13:10:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211568066441754524034413341069694645491","date":"2025-10-13T06:00:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-12T00:22:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-10T14:06:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-10T14:04:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Energy, Ecology and Environment","date":"2025-09-30T16:29:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"energy-ecology-and-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eeae","sideBox":"Learn more about [Energy, Ecology and Environment](http://link.springer.com/journal/40974)","snPcode":"40974","submissionUrl":"https://submission.nature.com/new-submission/40974/3","title":"Energy, Ecology and Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"df540467-c054-4d59-817a-5a88abceb372","owner":[],"postedDate":"October 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T12:15:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-24 09:26:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7753587","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7753587","identity":"rs-7753587","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}