# Thirty years of nitrogen dynamics in European agri-food systems: a territorial metabolic perspective

Ludovic Harter, Rasmus Einarsson, Jean-Christophe Lata, Julia Le Noë

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Ludovic Harter, Rasmus Einarsson, Jean-Christophe Lata, Julia Le Noë. Thirty years of nitrogen dynamics in European agri-food systems: a territorial metabolic perspective. Environmental Research Letters, 2026, 21 (4), pp.044009. 10.1088/1748-9326/ae452e hal-05534288

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# Thirty years of nitrogen dynamics in European agri-food systems: a territorial metabolic perspective

To cite this article: Ludovic Harter *et al* 2026 *Environ. Res. Lett.* 21 044009

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# Thirty years of nitrogen dynamics in European agri-food systems: a territorial metabolic perspective

Ludovic Harter<fnref n="1" />,<fnref n="2" />,* ID, Rasmus Einarsson<fnref n="2" /> ID, Jean-Christophe Lata<fnref n="1" /> ID and Julia Le Noë<fnref n="1" /> ID

E-mail: ludovic.harter@ird.fr

**Keywords:** agri-food systems, nitrogen budget, specialization trajectories, agro-environmental indicators, socio-ecological metabolism, Europe, GRAFS

Supplementary material for this article is available [online](#)

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Abstract

European agri-food systems face the dual challenge of reducing their environmental footprint, particularly nitrogen losses, while meeting the demands for both food and non-food biomass. Because future responses will have to build on the legacy of past trajectories, addressing this challenge requires a sound, fine-grained understanding of those trajectories. In the present study, we address this challenge by compiling a dataset (1990–2019) quantifying nitrogen flows across the agri-food systems of 120 European territories. Using this dataset, we apply the generalized representation of agri-food systems framework to classify agri-food systems into six types representing different degrees of specialization. Our results show that agricultural systems became increasingly specialized, rising from 22% of specialized territories in 1990 to 36% in 2019, indicating a growing reliance on external feed and fertilizer inputs. Most of these transitions occurred in eastern Europe, predominantly toward stockless cropping systems. Although these evolutions unfolded within broader processes of market liberalization, transition patterns also displayed clear path-dependency effects rooted in the material configuration of agri-food systems. This is evidenced by the analysis of transition frequencies, which shows that some transitions occurred repeatedly while others never occurred, pointing to the influence of initial system configurations on subsequent trajectories. By contrast, changes in agro-environmental performance were only weakly linked to system transitions. The increases in nitrogen use efficiency (from 0.57 to 0.70) and declines in nitrogen surplus (from 68 to 49 kg N ha⁻<fnref n="1" />) rather coincided with higher yields and increased nitrogen input application rates, pointing to the dominant role of factors not explicitly captured here, such as shifts in agronomic practices and pedo-climatic conditions. Taken together, these findings provide both a critical dataset and a robust understanding of recent territorial agri-food trajectories, thereby offering a solid empirical basis for anticipating future transitions and developing scenario-based analyses.

1. Introduction

Although essential for human nutrition, the agri-food
system significantly alters both nitrogen (N) and car-
bon cycles, which have major consequences for eco-
systems and climate. It is responsible for one-third of
global greenhouse gas emissions, driven by changes
in land use, enteric fermentation, manure manage-
ment, fertilizer production, and N volatilization [1].

It also contributes to biodiversity loss through habitat
destruction and eutrophication resulting from envir-
onmental N emissions [2]. These issues are becom-
ing increasingly critical as the demand for biomass
in non-food sectors (e.g. bioenergy) grows as part
of bioeconomic strategies in public policies [3]. This
is particularly evident in the European Union (EU),
where the development of a bioeconomy has become
a key public policy objective [4]. Although these

<footnote n="1">¹ Institute of Ecology and Environmental Sciences of Paris (iEES), UMR 7618 (Sorbonne Université, IRD, UPCité, UPEC, CNRS, INRAE), Paris, France</footnote>

<footnote n="2">² Department of Energy and Technology, Swedish University of Agricultural Sciences, Uppsala, Sweden</footnote>

<footnote>* Author to whom any correspondence should be addressed.</footnote>
strategies are frequently touted as means of reducing fossil fuel dependency and mitigating climate change, their implementation necessitates major adjustments in land use planning, biomass modes of production and consumption at all levels, from regional to continental [3, 5].

Against this backdrop, there is a need for a fine-
grained understanding of current agri-food config-
urations, since any future scenario will necessarily
build on the legacy of existing structures. Agricultural
transitions, in particular, will have to contend with
both biogeochemical and socio-technical legacies,
which may act either as constraints or as levers for
change. These legacy effects can be understood in
terms of path dependency. This notion, developed
in economics and political science in Arthur Brian's
seminal work [6], refers to processes in which current
configurations and future trajectories are strongly
shaped by past decisions and historical contingen-
cies through self-reinforcing mechanisms and lock-
in dynamics. Over the last two decades, this concept
has gained traction in environmental and agro-
nomic research. However, most studies investigating
path dependency effects of agricultural systems have
focused on farmers and policy choices [7], leaving
aside the possible lock-ins induced by the materi-
ality of agri-food systems such as livestock density,
the distribution of agricultural land between arable
lands, grasslands and permanent crops, and how
these material aspects of agri-food systems interact.
This research gap is particularly important because
the improvement or deterioration of agronomic and
environmental performances do not only reflect tech-
nical choices by farmers but are also shaped by the
evolution of the territorial structure of agri-food sys-
tems. As several studies have highlighted the sys-
temic links between territorial agricultural specializa-
tion and agro-environmental outcomes, showing that
the most specialized agricultural systems often have
the highest yields, but also the greatest environmental
footprint and the lowest nutrient use efficiency on a
per hectare basis [8–11].

The material dimensions of agri-food systems, shaped by both human activities and biogeochemical processes, can be understood through the concept of socio-ecological metabolism. By analogy with biological metabolism, the approach of socio-ecological metabolism examines how societies organize the extraction, transformation, and circulation of energy and materials from nature to sustain social functions, and how these flows impact ecosystems [12, 13]. This approach has been applied to assess agri-food system trajectories in various European countries [14–16] and provides a suitable framework to investigate how the material configuration of agricultural systems relates to their path-dependent dynamics and their agro-environmental performances over time. Yet, these connections remain insufficiently

understood, even though they are central to anti-
cipating transitions towards more sustainable agri-
cultural systems. The underlying idea—that the cur-
rent functioning and agro-environmental perform-
ances of agri-food system metabolism depend on
their past trajectories—is conceptually simple, but to
our knowledge, has never been empirically tested in
the European context. Indeed, while the premise is
straightforward, the method and data required to test
it are considerably more demanding.

A first challenge lies in the conceptualization of agri-food system trajectories, which requires identifying recurring patterns and defining consistent typologies capable of capturing structural differences between territorial agricultural systems. In the European context, such typologies have been developed to analyze the current state of agriculture and have revealed that the current European agricultural landscape is characterized by the coexistence of various farming systems at the territorial level [11]. On the one hand, highly specialized territories engaged in intensive cropping or livestock production are deeply integrated into global markets. These territories are heavily reliant on imported feed or fertilizers and oriented towards export [11, 17]. In contrast, multifunctional systems persist, often rooted in integrated crop-livestock farming with fodder or grassland-based feeding strategies and greater contribution of manure in crop fertilization. In these systems, nutrient cycles remain partly closed at the territorial level [11, 17, 18]. Other studies conducted at the territorial level in individual countries, such as France and Spain, offer a deeper historical perspective and highlight a strong tendency toward agricultural specialization in cropping or livestock systems [14, 19, 20].

A second challenge is methodological and data-
related, as analyzing the emergence and persistence
of territorial structures over time depends on suit-
able modeling frameworks and harmonized datasets
that enable consistent comparisons across space and
time. This includes examining *what* is produced (e.g.
crop mix, livestock types) and *how* it is produced,
taking into account the intensity and types of inputs
used. In this respect, N offers a relevant integrat-
ive metric for this purpose, as its flows are central
to both agronomic performance and environmental
impacts. A wide range of modeling approaches, such
as CAPRI [21], generalized representation of agri-
food system (GRAFS) [22], MITERRA-Europe [23],
and IMAGE [24], provide tools for assessing agro-
nomic and environmental performance in terms of
N at various spatial scales. While these models rely
on the same underlying publicly available datasets
and produce broadly consistent outputs [25], they
differ in their level of complexity, scope and data
requirements. In the context of long-term and large-
scale analyses, parsimony is a key criterion, as it
facilitates transparency and determines whether the necessary datasets are available, particularly at subnational scales and over extended time periods. From this perspective, GRAFS offers a suitable compromise, as it captures the systemic functioning of agri-food systems while relying on a limited set of input datasets. However, at the European level, the GRAFS model has been applied only at the national level since 1961 [26] and at the regional level for the 2014–2019 period [11]. This is due to the lack of a long-term harmonized dataset at the subnational level in Europe. To our knowledge, Batool et al [27] provided the only continuous and harmonized long-term reconstruction of subnational N inputs, outputs and budgets across Europe, based on the spatial downscaling of national data collated from the FAOSTAT database and other sources [28]. However, no database currently reconstructs long-term N flows at the subnational level using the statistical information directly available at that scale, notably via Eurostat, thereby limiting the ability to analyze territorial trajectories grounded in observed regional data.

Against this background, the present study addresses the following questions: how has agri-food metabolism evolved across European territories over the past three decades? Can path-dependent effects be identified in these dynamics, and how are they related to changes in agro-environmental performance? To answer these questions, we build a comprehensive dataset of N flows to implement the GRAFS model. We focus on the period from 1990 to 2019 because Eurostat began consistently collecting sub-national data from 1990 onward. Although major structural changes in European agriculture occurred prior to this date, successive reforms to the Common Agricultural Policy (CAP) over the past three decades highlight the relevance of this timeframe [29, 30]. Moreover, 1990 directly follows the fall of the Berlin Wall, marking the end of the Soviet era in the Eastern bloc and the beginning of transformations in their agricultural systems [31].

This approach allows us to first examine the temporal evolution of key variables in agri-food systems, then explores the territorial trajectories and their agro-environmental performance, and assesses both the frequency and the likelihood of the effective and potential trajectories. Finally, we discuss the limitations and blind spots of these results, considering the uncertainties inherent in the methodology and data, as well as the broader environmental, socio-political contexts that have shaped these trajectories and their performance.

## 2. Methods and data

This study applies the GRAFS typological framework to develop a socio-ecological metabolic understanding of recent trajectories in European

agri-food systems. To implement GRAFS, we constructed a continuous annual time series of European N flows and budgets at a territorial scale (mainly sub-national). This was done using public data directly at their native resolution, primarily from Eurostat, complemented by transparent procedures for gap-filling and error correction.

### 2.1. GRAFS: a metabolic perspective on agri-food system

The GRAFS approach was initially developed as a biogeochemical accounting framework to quantify N and P flows within agri-food systems across spatial scales, ranging from the farm to the national and continental levels [22, 32] (figure 1(a)). GRAFS largely relies on input data regarding land-use areas, crop areas and production, livestock numbers and densities, and commercial fertilizer use, which are mostly available from official statistics. The model represents seven main compartments through which N is flowing: arable land, permanent cropland, grassland, livestock, the local human population, foreign territories to which surpluses are exported, and the surrounding environment where losses occur. Whenever possible, the allocation of N flows between compartments is based on documented coefficients. This is the case, for instance, for the allocation of commercial fertilizer and manure between arable land, permanent croplands and grasslands (see supplementary material).

A typological classification was introduced to characterize the structural configurations of agri-food systems. This classification is based on three main dimensions: (1) the level of production specialization, (2) the capacity to meet local demand for food and feed, and (3) the degree to which systems close their N cycle or depend on external inputs. It allows characterizing different types of agricultural metabolism while simultaneously deriving agro-environmental performance indicators expressed in terms of N, particularly N use efficiency (NUE, defined as N removals divided by N total inputs) and N surpluses (NS, defined as N inputs minus N removals). These two indicators jointly capture both environmental pressures and the performance of agri-food systems, assuming that NS is a proxy for N losses to the environment. The typology was applied annually to each territory using a decision tree approach (figure 1(b)), which was originally developed for France [8, 14], later adapted to Spain [19], and more recently scaled up to cover the whole of Europe for the period 2014-2019 [11].

Our typology defines six types of territorial agri-food systems (figure 1(b)). (1) Urban systems are characterized by local food demand that exceeds local production, including edible animal products. This makes food imports the dominant component of the territorial system. (2) Specialized intensive livestock systems exhibit high livestock densities and
<figure><img src="image_6.png" /></figure>

insufficient local feed production to meet animal feed requirements, leading to a strong dependence on imported feed. Mixed crop-livestock systems are characterized by strong integration between crops and livestock, with animal manure contributing a significant proportion of N to croplands and locally produced feed meeting a large proportion of livestock requirements. (3) Grass-based systems predominantly rely on permanent grasslands to supply at least half of the livestock feed, whereas (4) forage-based systems meet at least one-third of the demand for livestock feed through local cropland production. (5) Disconnected crop-livestock systems combine crop

and livestock activities within the same territory but have weak functional links: manure does not account for a significant share of cropland fertilization and livestock feed comes largely from grassland or is imported. Finally, (6) stockless specialized cropping systems are dominated by crop production.

## 2.2. Spatial resolution

The spatial resolution of the dataset is a critical trade-off. Although finer scales allow for a more precise representation of spatial heterogeneity, they also amplify the impact of uncertainty in the input datasets. Indeed, spatially-gridded input datasets
<table><caption>**Table 1.** Summary of sources and estimation methods used for key variables included in the dataset. See supplementary material for further details.</caption><thead><tr><td>Variable</td><td>Source and estimation method</td></tr></thead><tbody><tr><td>Land use</td><td>Eurostat, EuropeAgriDB [28], FAOSTAT</td></tr><tr><td>Crop harvested quantities</td><td>Eurostat, EuropeAgriDB [28]</td></tr><tr><td>N content in crops</td><td>Constant values for 33 crop categories [35]</td></tr><tr><td>Livestock number</td><td>Eurostat, FAOSTAT</td></tr><tr><td>N excretion factors</td><td>Country-specific for 8 animal categories [36]</td></tr><tr><td>N mineral fertilizer quantities</td><td>Eurostat, EuropeAgriDB [28], national inventories</td></tr><tr><td>Biological N fixation</td><td>Constant coefficients for 4 crop categories multiplied by their N yield [35, 37]</td></tr><tr><td>Total N deposition</td><td>Dry and wet from the EMEP model [38], weighted by the arable land in CORINE 2018</td></tr><tr><td>N volatilization at manure application</td><td>Constant coefficient (0.21), IPCC Tier 1</td></tr><tr><td>N volatilization at mineral fertilizer application</td><td>Constant coefficient (0.11), IPCC Tier 1</td></tr><tr><td>Population</td><td>Eurostat</td></tr><tr><td>Animal production</td><td>Eurostat, FAOSTAT</td></tr><tr><td>N content in animal production</td><td>Constant values for each animal (edible and non-edible), eggs and milk [14]</td></tr><tr><td>Permanent grassland yield</td><td>Subnational spatially explicit yields, temporally constant [11]</td></tr></tbody></table>

result from a down-scaling model using regional or even national inventory datasets; the uncertainty in these finer datasets is inevitably higher than in the regional to national datasets they depend on [33]. We required a resolution that captures key heterogeneity while maintaining reliable aggregate estimates. The desired resolution would also correspond to scales at which land-use decisions and policy interventions are actually made and thus be integrable into European national and continental assessments and policy frameworks. Territorial divisions defined by the Nomenclature of Territorial Units for Statistics (NUTSs), as established by the European Commission in 2003, meet these criteria [34]. Because the utilized agricultural area (UAA, defined as the total area of arable crops, permanent grasslands, and permanent crops) per NUTS region varies substantially across Europe, different NUTS levels were selected depending on the country to meet the UAA requirement of 1–2.5 Mha, following Billen et al [11]. For Albania, Bosnia and Herzegovina, Croatia, Estonia, Finland, Latvia, Luxembourg, the Netherlands, North Macedonia, Norway, Serbia, Slovakia, Slovenia, and Switzerland, the national scale (NUTS 0) fell within the target range. Czechia, Denmark and Lithuania slightly exceeded the upper bound, but were retained at NUTS 0. For Austria, Belgium, Bulgaria, Germany, Greece, Hungary, Italy, Poland, Sweden and the United Kingdom, NUTS 1 units were selected. For France, Ireland, Portugal, Romania and Spain, NUTS 2 units were selected.

It should be noted that the NUTS nomenclature was selected by country for consistency. As a result, some regions correspond to urbanized areas with very low UAA. To ensure relevance in the analysis, regions with less than 50 kha of cropland were excluded from the dataset. This applies in particular

to BE1–Brussels, DE3–Berlin, DE5–Bremen, DE6–Hamburg, and UKI–London. This results in a total of 120 geographic units, which we will refer to as ‘territories’ throughout the article. These territories cover the EU-27, Albania, North Macedonia, Norway, Serbia, Switzerland, and the United Kingdom. While some regions correspond to entire countries, the term ‘territory’ is used consistently throughout the paper for the sake of simplicity.

## 2.3. Data source, compilation, and processing

To implement the GRAFS framework, we developed a dataset that extends previous European scale studies by providing harmonized regional N flows across agri-food systems, as well as N budgets for arable land, permanent cropland and permanent grassland from 1990 to 2019, based on publicly available data (table 1). The dataset includes N quantities in harvested crops and their corresponding areas, classified by crop type. It also reports total N input from synthetic fertilizers, manure application, symbiotic fixation, atmospheric deposition and ammonia volatilization. It also provides estimates of total feed and food imports, animal production and the allocation of N flows from harvested cropland to local human and livestock populations, as well as to exports and imports.

To ensure consistency and temporal continuity, we integrated Eurostat data at their native spatial resolution with data from other sources, applying standardized correction and gap-filling procedures and assigning processing flags to each data point to indicate whether it is unprocessed, gap-filled (if so, by which method), or corrected. Unprocessed Eurostat data account for 44% of livestock entries, 22% of synthetic fertilizer quantities, and 37% and 33% of harvested area and production, underscoring the substantial incompleteness of these public datasets or
their need for systematic correction. Original gap-
filling and correction procedures were applied to 35%
of livestock data, 78% of synthetic fertilizer data,
and 51% and 55% of harvested area and production.
These procedures go beyond simple linear interpola-
tion. Whenever regional data were available, we used
them to derive ratios between regional and national
values. Rather than interpolating absolute values, we
interpolated these ratios and then applied them to
national data on production, fertilization, or harvest-
ted area. Linear interpolation was used only to fill
the remaining gaps for which no external informa-
tion was available in large-scale datasets. These cases
accounted for 18%, 0%, 6%, and 4% of livestock, fer-
tilizer, harvested area, and harvested production data,
respectively (see figure S1). Supplementary materials
provide a detailed description of the data-processing
workflow.

3. Results

**3.1. General and territorial trends in Europe**

Across Europe as a whole, the total N harvested
from arable land increased slightly between 1990 and
2019, rising from 10.8 Tg N to 11.4 Tg N respect-
ively (figure 2(a)). During this period, the area of
arable land decreased while permanent crops and
permanent grasslands remained roughly constant. As
a result, UAA decreased from 211 Mha to 176 Mha
(figure 2(c)). This decline in UAA is well documented
and has been associated with the progressive con-
version of agricultural land into managed forests
[39, 40]. Simultaneously, yields have increased in
most crops so that the aggregate N harvest remained
roughly constant (figure 2(d)).

Over the same period, the total N fertilization
of arable land did not exhibit a clear long-term
trend. Following a sharp decline in the early 1990s—
primarily due to the reduced use of synthetic fer-
tilizers in eastern Europe—total N inputs decreased
from 19 Tg in 1990 to 17 Tg in 1992 and then to
16.3 Tg in 2019. This trend is strengthened by the
relative loss in livestock numbers across Europe,
falling from 139 million Livestock Units (LU, here
defined as livestock excretion of 85 kg of N per year,
roughly equivalent to one dairy cow [8]) in 1990
to 118 million LU in 2019. Consequently, livestock
excretion declined from 11.8 Tg N in 1990 to 10.0 Tg
N in 2019, and N inputs from manure application on
arable land decreased from 4.5 Tg to 4.0 Tg over the
same period. Despite the lower N inputs, the average
N fertilization rates increased: from 156 kg N ha⁻¹
in 1992 to 165 kg N ha⁻¹ in 2019, due to the simul-
taneous decrease in the area of arable land. At the
same time, the proportion of imported feed N has
remained relatively stable over the past three decades,
accounting for around 20% of total livestock feed N
inputs (figure 2(b)). It is important to note that, at the

European scale, imported feed is calculated as the sum
of territorial import requirements needed to close
local supply-demand gaps. Consequently, this metric
does not strictly represent extra-European imports,
but rather reflects the characteristic degree of import
dependence of each territory.

A significant evolution is the reduction in NS
from arable land, which decreased by over 2 Tg, fall-
ing from 67.6 kg N ha⁻¹ in 1990 to 49.4 kg N ha⁻¹ in
2019. This decrease was accompanied by a notable
improvement in arable NUE, which increased from
0.57 to 0.70 as well as arable land yields increased by
26.5 kg N ha⁻¹ since 1990.

These general trends, however, conceal hetero-
geneous evolution. The spatial distribution of aggreg-
ated arable yield across Europe reveals a clear pat-
tern (figure 3(a)): yields are highest in the western
and central territories (e.g. Austria, Denmark, France,
Germany, the Netherlands, the UK, and northern
Italy) and lowest in the eastern, northern, and south-
ern parts of Europe, including the Iberian Peninsula,
southern Italy and Greece. In terms of temporal vari-
ability, arable cropland yields have increased in most
European territories over the past 30 years, with no
clear spatial pattern (figure 3(g)).

The density of livestock varies across territories,
with the highest concentrations observed in Brittany
(France), Catalonia (eastern Spain), Denmark,
Germany, the Netherlands, Norway, northern Italy,
and Switzerland (figure 3(b)). In the Netherlands, for
example, livestock density reaches 2.5 LU per hectare
of UAA. These territories with high livestock dens-
ities often have a greater environmental footprint
in terms of NS (figure 3(f)) as manure production
often exceeds the N requirements of local cropland.
The spatial distribution of the proportion of mineral
fertilization relative to total N inputs does not show
a clear geographical pattern (figure 3(d)). However,
territories with a lower proportion of mineral fer-
tilizers generally coincide with areas of high live-
stock density, where N requirements are met largely
through manure application. Conversely, temporal
trends in fertilizers paint a different picture: while
most northern, southern and western European ter-
ritories exhibit either no clear trend or a slight decline
in mineral fertilizer share over time, eastern territor-
ies show a marked increase of up to 40% in Lithuania
(figure 3(j)).

Total N input rates tend to be higher in ter-
ritories with a high livestock density, where struc-
tural manure surpluses do not always preclude the
use of synthetic fertilizers (figures 3(b) and (c)).
This indicates that territorial recycling of manure
is likely limited by specialization at the farm level
or otherwise at the intra-territorial level, due to
logistic barriers to manure transportation [41]. While
most Central European regions exhibit an overall
decline in N input rates, the eastern territories show
<figure><img src="image_7.png" /><figcaption>**Figure 2.** (a) N harvest from arable land for the aggregated crop categories, along with N inputs from different fertilization inputs, summed over all territories. The area above the red line represents the N surplus (inputs minus outputs); (b) livestock-related N flows, aggregated across all territories, including excretion, edible, and non-edible animal production. The sum of excretion and production approximates total animal N ingestion, from which imported feed is estimated. Note that the imported feed shown represents the sum of imports in each territory; (c) land areas: arable crops, permanent crops, and permanent grassland, summed across all territories; (d) N yield for the 12 aggregated arable crop categories, calculated as the total N harvested summed over all territories divided by the total crop-specific area. Aggregated arable yield was derived by dividing the sum of harvested quantities across all arable crops by the corresponding sum of harvested areas.</figcaption></figure>

increasing inputs, primarily driven by rising mineral fertilizer use (figures 3(i) and (j)), as previously shown. Additionally, parts of the Iberian Peninsula have experienced an increase in input rates, coinciding with growth in livestock density and excretion (figure 3(h)), in line with findings from studies focusing specifically on Spain [19].

## 3.2. Typological trajectories of European agri-food metabolisms

The evolution of these key variables is part of broader systemic trajectories, which we were able to capture through the typological approach. In 1990, disconnected crop-livestock systems were the most represented type, with 51 European territories accounting for 42% (figure 4). These territories combine both livestock and cropping systems without functional links. As a consequence, intensive cropping systems coexist with relatively extensive livestock that are only weakly connected to cropland, resulting in hybrid systems that carry internally contradictory dynamics. At that time, 38% of the territories were characterized as grass-based or fodder-based integrated crop and livestock farming systems while only a minority of European territories (22%) had highly specialized systems, including 10 territories with livestock-intensive systems, 17 territories with stockless cropping systems. By construction of the typology, these systems were characterized by a strong reliance on externally imported N (imported feed or synthetic fertilizers) (figure 1(b)).

The majority of the territories maintained the same type of metabolic system between 1990 and 2019. However, 36% (43 territories; figure 4) underwent a change in system type during the period. It is also worth noting that the number of territories classified as urban increased over the period, from 9 to 11. Hereafter, we use the term ‘trajectory’ to refer to all combinations, including cases without change, and ‘transition’ specifically to denote a change in type over time.

At the European scale, these transitions resulted in a clear trend toward specialization in either stockless cropping systems or intensive livestock farming: 36% of territories revealed strong specialization in 2019 (against 22% in 1990). These evolutions are largely explained by the decline of disconnected crop-livestock systems from 49 to 39 territories and fodder-based integrated crop and livestock systems from 13 to 7 territories concomitant to the doubling of cropping-intensive systems from 17 to 34 territories (figure 4).

These trajectories are not evenly distributed across Europe: we identified three main spatial patterns (figure 4). In eastern Europe, many territories belonged to the crop-livestock disconnected type in the 1990s (11 out of 31), and their number had decreased by 8 by 2019. By 2019, the majority (18 out of 31) had transitioned towards stockless specialized cropping systems compared with only 7 in 1990, reflecting a continued decline in livestock and an increased specialization toward crop production for human consumption. In the Mediterranean, most
<figure><img src="image_8.png" /><figcaption>**Figure 3.** Spatial distribution of six N indicators across European territories. Panels (a)–(f) show averages for the period 2015–2019: (a) aggregated arable crop yield, calculated as the total harvested output divided by the total arable land area; (b) livestock density, expressed as the number of livestock units (each excreting 85 kg N yr⁻¹) per hectare of utilized agricultural area (UAA); (c) total N inputs to arable land; (d) share of mineral fertilizer in total N inputs to arable land; (e) NUE computed as output/in-put ratio on arable land; and (f) NS on arable land. Panels (g)–(l) show the change in these indicators between the periods 1990–1995 and 2015–2019. Positive values indicate an increase over the period, while negative values indicate a decrease.</figcaption></figure>

territories (22 out of 36) were characterized by dis-
connected crop-livestock systems in the 1990s. This
typological structure, in contrast to eastern territo-
ries, remained partly stable or changed into more
focused livestock systems, either intensive or mixed
with grassland or fodder production (from 11 to 12
out of 36). While the typology remained rather stable
in these regions, these systems followed a trajectory
of intensification: UAA declined from 64.6 Mha in
1990 to 50.8 Mha in 2019, mainly due to reductions
in arable land and permanent grasslands. Meanwhile,
overall crop production remained stable, reflecting

yield gains. Finally, the agri-food system structure in
western and northern Europe was more heterogen-
eous in the 1990s and underwent no major typolo-
gical shifts during the study period, suggesting that
key transitions had occurred prior to the period
examined, in line with previous research [14, 26].

3.3. Path-dependent dynamics and
agro-environmental performances

Based on the territorial trajectories, we explore the
existence of path-dependent effects that may have
shaped them during the studied period. Figures 5(a)
<figure><img src="image_9.png" /><figcaption>**Figure 4.** Agricultural typology at the territorial level in 1990 (left panel) and 2019 (right panel). Classes are defined as follows: 'SCS'—stockless specialized cropping systems; 'LVK'—intensive livestock-dominated systems; 'MXG'—mixed grassland-livestock systems; 'MXF'—mixed fodder-livestock systems; 'DSG'—disconnected crop-livestock systems; and 'URB'—urban territories (hatched). Territories outlined in red correspond to the eastern group and those outlined in blue correspond to the Mediterranean group.</figcaption></figure>

and (b) presents the transition counts and probabilities across all territories. It highlights the influence of the initial state on the trajectory pattern: some typologies remain highly stable over time, whereas others tend to change. The stockless cropping system is the most stable typology, with only one observed transition to the disconnected crop-livestock system type (figure 5(a)). In contrast, mixed grass-livestock systems, disconnected crop-livestock systems, and intensive livestock-dominated systems exhibit higher levels of change, with stable trajectories representing 58%, 59% and 71% of territories, respectively. The mixed fodder-livestock system is the least stable, with only 40% of territories retaining the same typology over the study period.

Our results show that transition pathways may be influenced by the initial typology. Certain shifts never occurred; for example, no territory transitions from an intensive livestock system to a stockless specialized system and vice-versa, or from a fodder-livestock integrated system to a grassland-livestock integrated system. By contrast, certain transitions had a higher frequency of occurrence than others over the past 30 years. Specifically, disconnected crop-livestock systems, fodder-based and grass-based integrated crop-livestock systems had 25%, 30% and 12% probabilities, respectively, of transitioning towards stockless cropping systems. Similarly, intensive livestock systems, fodder-based and grass-based integrated crop-livestock systems had 25%, 20% and 12% probabilities, respectively, of transitioning towards disconnected crop-livestock systems (figure 5(b)). These results indicate that the disconnected metabolic profile functions as a pivotal transition type, acting as a recurrent intermediate configuration within the overall dynamics. It is both a frequent endpoint for several system

types and a common starting point, most notably for transitions toward the stockless cropping type, which accounts for 13 territories and represents the main transition pattern.

Changes in agro-environmental performance associated with each trajectory must be interpreted in relation to the variability observed among the territories following the same trajectory. This is useful to ensure that the indicator values likely reflect characteristics intrinsic to the trajectory itself rather than characteristics resulting from the specific levels of agricultural intensity in the territories. Figures 5(c) and (d) presents the mean changes in arable NUE and NS in relation to the metabolic trajectories, along with the standard deviation among them. Notably, stable trajectories may exhibit changes in these indicators, but the very high variability observed (coefficients of variation up to 600%) limits the robustness of these differences and points to heterogeneous territorial shifts within the same trajectory. Among transitions showing substantial changes in indicator values, for which the coefficient of variation is below 100%, most transitions originating from disconnected crop-livestock systems (90% representing 19 territories) led to increases in arable NUE and reductions in arable NS (figures 5(c) and (d)). In particular, transitions to stockless cropping (13 territories, main transition pattern, see figure 5(a)) increased arable NUE by 0.18 and reduced NS by 29 kg N ha⁻¹ yr⁻¹ on average. The fact that this transition represents the dominant pattern, combined with the changes in indicator values associated with relatively low variability, supports the view that this specific system transition may be an important driver of the observed agro-environmental improvements.
<figure><img src="image_10.png" /><figcaption>b) Transition probabilities (1990-1994 → 2015-2019)</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>c) Δ mean NUE per transition (Δ ratio)</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>d) Δ mean NS per transition (Δ kg N/ha)</figcaption></figure>

<figure><img src="image_13.png" /></figure>

**Figure 5.** Transition matrices between typologies from 1990–1994 to 2015–2019. (a) Transition frequencies, i.e. the number of territories following each trajectory. (b) Transition probabilities, showing the likelihood of moving from one typology to another (or remaining in the same). (c) Change in arable NUE, computed as the difference between the 2015–2019 and 1990–1994 averages. (d) Change in arable NS, computed in the same way. Error values in (c) and (d) represent one standard deviation among all territories sharing the same trajectory. Typologies are defined as follows: ‘SCS’—stockless specialized cropping systems; ‘LVK’—intensive livestock-dominated systems; ‘MXG’—mixed grassland-livestock systems; ‘MXF’—mixed fodder-livestock systems; ‘DSG’—disconnected crop-livestock systems.

# 4. Discussion

## 4.1. Limitations and uncertainties

Our results are inherently associated with both limitations and uncertainties. As discussed by Oenema et al [42], uncertainties in the model results arise from both structural uncertainties related to the model's hypotheses and operational uncertainties associated with data quality. In the present study, structural uncertainties relate to the thresholds used to define the typology, which constitute a central element of our results. However, the value of the typology lies precisely in its limitation: although somewhat arbitrary, particularly regarding the threshold values that determine when a system shifts from one type to another, this classification provides a simplified visualization of systemic differences and trajectories by reducing the continuous spectrum of N flow configurations and intensities to a discrete representation.

Another structural uncertainty concerns territorial spatial resolution: the resulting classes describe an ‘average’ territorial metabolism that aggregates diverse farm structures, some of which may exhibit different degrees of specialization. As a consequence,

the territorial level may suggest more integrated systems (in the case of mixed crop-livestock farming systems) than is actually the case: manure availability may lead to local structural surpluses, while available grass and forage may not be optimally allocated to local livestock. Nonetheless, we worked with the finest spatial resolution currently available. A final source of structural uncertainty relates to the assumptions used to allocate harvested N flows between local human consumption, local livestock intake, and exports. This type of uncertainty is difficult to quantify. Future research focusing on individual territorial trajectories could help reduce these uncertainties by further investigating the actual allocation of harvested products, an approach that was not feasible for 120 territories over a 30 year period.

Operational uncertainties mainly stem from the quality of Eurostat databases and from the gap-filling and correction procedures applied (see supplementary materials). These uncertainties are difficult to quantify, but the systematic treatment of implausible or inconsistent values makes the harmonized database a major outcome of the study. While further improvements in data reliability remain possible, for
instance, through a systematic comparison of Eurostat data with national statistical sources at the regional level, such an effort was beyond the scope of the present study.

Finally, a limitation of the present study lies in the choice of agro-environmental indicators, which necessarily set aside other dimensions of sustainability, such as greenhouse gas emissions from livestock methane emissions and fertilizer manufacturing, agro-biodiversity losses associated with pest management, or the externalized environmental footprint linked to imported feed and animal products. As a result, improvements in arable NUE or NS should be interpreted with caution, as they may coincide with, or even result from, the displacement of other environmental pressures. These uncertainties and limitations, now clearly identified and discussed, do not undermine the relevance of the results, which can therefore be examined in detail.

## 4.2. Accounted and unaccounted material drivers of system evolutions

Our results support the existence of path-dependence mechanisms shaping the evolution of agri-food systems over the period 1990–2019. In particular, the hybrid disconnected crop-livestock type appears to be a pivot system, from which territories tend to move either toward stronger specialization or toward more integrated configurations. This class can indeed accommodate a wide range of metabolic shifts because its material configuration does not pre-determine a specific direction of change. Similarly, the mixed grass-livestock type shows high stability over time compared to other types, except the stockless types. This likely reflects physical constraints: territories dominated by permanent grasslands are poorly suited to crop production, limiting the range of possible transitions. Territorial trajectories toward livestock or cropping specialization therefore appear to be strongly influenced by pre-existing orientations rooted in the materiality of metabolic profiles, which could also be determined by topography: flat, expansive landscapes are more commonly devoted to crop production, while territories with more fragmented terrain tend to support grassland and livestock systems [18]. Transitions that did not occur further reinforce the idea of the existence of material path-dependency effects. For instance, we observed no transitions from livestock-specialized systems to stockless cropping systems and vice-versa, which would imply strong shifts in livestock density, feed import, land occupation and fertilization management. Likewise, no transitions were observed from stockless systems to mixed fodder or mixed grass-livestock types. These absences suggest that the material infrastructures and the land use and land cover required to sustain livestock production or stockless cropping constitute structural constraints that, in

interaction with market and policy conditions, limit the feasibility of certain transitions.

Our findings also indicate that, across most territories, changes in arable agro-environmental performance are only weakly influenced by typological trajectories. The main exception is the transition from disconnected crop-livestock to stockless cropping systems, which is associated with clear improvements in performance. Rather, the general rise in yields and fertilizer application rates over the study period likely constitutes the primary driver of shifts in arable NS and NUE. This is unexpected, as higher fertilization rates are generally known to increase yields, typically at the cost of declining NUE and higher NS (as shown in figures 3(k) and (l)), due to diminishing marginal yield gains with increasing fertilization [43]. These results suggest that other factors came into play to drive increased crop yields across Europe since 1990. Possible causes of this evolution may be genetic improvement in crop varieties [44], which may not have reached their plateau yet [45], as well as improved agronomic practices, such as the expansion of irrigated areas, especially in water-stress zones [46].

## 4.3. Socio-political dependencies

Our results reveal both a strong trend toward the specialization of agricultural systems, particularly in countries of the former Eastern bloc, and clear forms of path dependence in these transitions, rooted in the material organization of farming systems, here captured through the configuration of N flows, that is, their agricultural metabolism. However, these trajectories cannot be understood solely through their metabolic dimensions. They also unfolded within a specific socio-political context marked by the successive CAP reforms following the Uruguay Round (the final GATT negotiations before the creation of the World Trade Organization in 1995), which liberalized agricultural markets and drove the decline of protectionist policies. These reforms followed intense negotiations, as competition from Europe's import barriers and export subsidies was becoming increasingly contentious for the US and was deemed unfair by many Global South countries [47]. This pushed for integration into global markets while adding 'green' measures that simultaneously conferred political legitimacy on the policy and helped maintain market distortions, insofar as these standards could also be used to justify restricted access to the European market [30]. In this new context, increased competition from US cereals and Latin American soybeans in the global market has encouraged producers to adopt economies of scale to maintain competitiveness, thereby reinforcing territorial and farm specializations [48]. Indeed, integrated crop-livestock systems generally require more labor than specialized systems, which can limit investment capacity in machinery and, consequently, productivity [10, 49].
Furthermore, tariff reductions led to a significant increase in soybean imports, resulting in a further concentration of N flows through imported feed in a few livestock-specialized territories. This may also explain the decrease of fodder-based integrated crop-livestock system classes.

The distinct trajectory of eastern territories can be understood within their specific historical and socio-political contexts. After WWII, eastern countries came under the Soviet Union influence and implemented socialist reforms including land collectivization, large-scale state or cooperative farming structures, and extensive industrialization. In agriculture, these transformations promoted mechanization, centralized planning and the widespread use of synthetic fertilizers, which also contributed to increased yields [31]. However, the collapse of the socialist regimes in the early 1990s led to a sharp reduction in mineral fertilizer use (by an average of 50% [28]). This was due to the end of public subsidies and agrarian reforms that led to the dismantlement of large state farms and the multiplication of small-holder farming with hardly any capital. This resulted in a significant decline in crop yields [31]. Since that period, many of these countries have progressively realigned their agricultural production with market-oriented systems, mainly stockless intensive cropping systems (figure 4). This transition has been accompanied by a gradual increase in mineral fertilizer use (figure 3(j)).

### 4.4. Implications for anticipating future agricultural transitions

When interpreted in the social and political context that shaped them, our results provide insights to better anticipate the likely trajectories of future agri-food transitions in an increasingly uncertain global environment. Since the election of Donald Trump in 2024, the US has moved toward a more protectionist trade policy, restricting access for EU products to the US market, with particularly strong effects on wine and high-value food products such as meat and dairy products. Such restrictions may further accelerate the erosion of the historical food power of both the EU and the US in global markets, particularly for bulk agricultural commodities, to the benefit of emerging countries such as Russia and Brazil [50, 51]. At the same time, successive EU reforms continue to reinforce agricultural specialization, maintaining an export-oriented model under open trade conditions. This is illustrated notably by the endorsement of the *Mercosur* agreement, which raises questions about the future of the European intensive livestock sector. Under these conditions, this sector may become increasingly less competitive, as production costs and environmental standards are generally lower in Latin American exporting countries, while a growing share of environmental pressures could effectively be displaced through further imports of animal products

and feed. Moreover, the EU's commitment to the bioeconomy as a strategy to reduce carbon emissions implies a substantial expansion of crop production for non-food uses [4]. Such a shift is likely to further accelerate transitions toward stockless cropping systems, which are both better aligned with prevailing market incentives and more compatible with the objective of supplying increasing volumes of biomass for non-food applications. In this context, our results highlight the pivotal role of disconnected crop-livestock systems which, as the largest relative share of agricultural territories in Europe, constitute a central hinge for future agri-food transitions. By identifying the metabolic configurations that constrain and facilitate transitions, our analysis not only suggests that these systems may increasingly evolve toward intensive stockless cropping systems, but also highlights the European territories where such transitions are most likely to occur—a hypothesis that only future trajectories can confirm or refute.

### 5. Conclusion

First, we show that over the last thirty years, despite contrasting territorial trajectories, the agri-food metabolism of European territories has followed a general trend toward increasing specialization, with a particularly pronounced shift toward intensive stockless cropping systems in eastern Europe. Second, our analysis highlights that these transitions exhibit a clear path-dependence effect rooted in the material configuration of metabolic systems. This path dependence is captured through the analysis of transition frequencies, which reveals that some transitions occurred frequently while others never occurred, pointing to the fact that initial system configurations influenced subsequent trajectories and final configurations. Third, although we observed a general trend towards lower arable NS and improved NUE, our results indicate that system transitions are only weakly associated with changes in agro-environmental performance of arable land.

These results rely on the construction of a continuous and comprehensive dataset, building on the rich information provided by *Eurostat* at the sub-national level. However, as the *Eurostat* dataset presents important discontinuities and inconsistencies, assembling such a dataset required the implementation of an original, careful, and fully traceable procedure for gap-filling and data correction. The resulting database thus constitutes a result in itself, which can be used for further investigation and refinement. For instance, although the present study did not aim to examine each territorial trajectory in detail, the dataset we compiled provides a solid foundation for quantitatively informed monographs of territorial agricultural pathways and offers a valuable resource for future scenario modeling.
In the present study, the exploration of key vari-
ables relevant to our research questions further
revealed that the observed trend toward lower NS
and improved NUE occurred simultaneously with
higher yields and increased N input application rates.
This finding is somewhat counterintuitive, as lower
fertilization rates are generally expected to lead to
a deterioration of NUE and NS. This suggests that
these changes are largely driven by shifts in agro-
nomic practices or by pedo-climatic factors, rather
than by system transitions themselves, factors that can
be more explicitly examined in future research using
the dataset compiled here.

Overall, these findings and analyses provide a
fine-grained understanding of recent trajectories of
territorial agri-food systems and help delineate plaus-
ible pathways for future transitions. They also high-
light the value of a metabolic perspective for uncov-
ering the drivers of agri-food system dynamics and
for anticipating the challenges and opportunities
involved in steering European agriculture toward
more sustainable configurations.

Data availability statement

The data that support the findings of this study are
openly available at the following URL/DOI: https://
doi.org/10.23708/4FAPMG [52].

Data reuse is granted under CC-BY license.
The complete data processing and figure construc-
tion workflow is archived on Software Heritage and
can be accessed via the following identifier: swh:1:
rev:eefbaed26cad541f9d9b702b358deab082405c3a.

Funding

This work forms part of SLAM-B (Scenario labs for
designing and assessing territorial bioeconomy tra-
jectories towards carbon neutrality), a project within
the exploratory research programme FairCarboN. It
has received government funding managed by the
Agence Nationale de la Recherche (ANR) under the
France 2030 programme (reference ANR-22-PEXF-
0003).

Conflict of interest

The authors declare no competing interests.

Author contributions

Ludovic Harter ID 0009-0002-7417-7821

Conceptualization (lead), Data curation (lead),
Formal analysis (lead), Methodology (lead),
Software (lead), Visualization (lead), Writing –
original draft (lead), Writing – review &
editing (lead)

Rasmus Einarsson ID 0000-0002-7587-6280

Conceptualization (supporting),
Methodology (supporting), Resources (supporting),

Supervision (supporting), Writing – review &
editing (supporting), Data curation (supporting)

Jean-Christophe Lata ID 0000-0002-1094-4625

Project administration (supporting),
Resources (supporting), Writing – review &
editing (supporting)

Julia Le Noë ID 0000-0001-8816-5800

Conceptualization (supporting), Funding
acquisition (lead), Methodology (supporting),
Project administration (lead),

Resources (supporting), Supervision (lead), Writing
– review & editing (supporting)

References

[1] Crippa M, Solazzo E, Guizzardi D, Monforti-Ferrario F, Tubiello F N and Leip A 2021 Food systems are responsible for a third of global anthropogenic GHG emissions *Nat. Food* 2 198–209

[2] Leip A *et al* 2015 Impacts of european livestock produc-
tion: nitrogen, sulphur, phosphorus and greenhouse gas
emissions, land-use, water eutrophication and biodiversity
*Environ. Res. Lett.* **10** 115004

[3] Scarlat N, Dallemand J-F, Monforti-Ferrario F and Nita V 2015 The role of biomass and bioenergy in a future bioeconomy: Policies and facts *Environ. Dev.* **15** 3–34

[4] COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE EUROPEAN COUNCIL, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE, THE COMMITTEE OF THE REGIONS AND THE EUROPEAN INVESTMENT BANK a clean planet for all a european strategic long-term vision for a prosperous, modern, competitive and climate neutral economy 2018 (available at: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52018DC0773)

[5] Priefer C, Jörissen J and Frör O 2017 Pathways to shape the bioeconomy *Resources* **6** 10

[6] Arthur W B 1989 Competing technologies, increasing returns and lock-in by historical events *Econ. J.* **99** 116

[7] Chhetri N B, Easterling W E, Terando A and Mearns L 2010 Modeling path dependence in agricultural adaptation to climate variability and change *Ann. Associat. Am. Geographers* **100** 894–907

[8] Noë J L, Billen G and Garnier J 2017 How the structure of agro-food systems shapes nitrogen, phosphorus and carbon fluxes: the generalized representation of agro-food system applied at the regional scale in france *Sci. Total Environ.* **586** 42–55

[9] Dorninger C, Abson D J, Fischer J and von Wehrden H 2017 Assessing sustainable biophysical human-nature connectedness at regional scales *Environ. Res. Lett.* **12** 055001

[10] Abson D J 2019 Chapter 19 - the economic drivers and consequences of agricultural specialization *Agroecosystem Diversity* ed G Lemaire, P C D F Carvalho, S Kronberg and S Recous (Academic) pp 301–15

[11] Billen G, Aguilera E, Einarsson R, Garnier J, Gingrich S, Grizzetti B, Lassaletta L, Noë J L and Sanz-Cobena A 2024 Beyond the farm to fork strategy: methodology for designing a european agro-ecological future *Sci. Total Environ.* **908** 168160

[12] Haberl H *et al* 2006 From LTER to LTSER: conceptualizing the socioeconomic dimension of long-term socioecological research *Ecol. Soc.* **11** 13

[13] Erb K-H 2012 How a socio-ecological metabolism approach can help to advance our understanding of changes in land-use intensity *Ecol. Econ.* **76** 8–14
[14] Noë J L, Billen G, Esculier F and Garnier J 2018 Long-term socioecological trajectories of agro-food systems revealed by n and p flows in french regions from 1852 to 2014 *Agric. Ecosyst. Environ.* **265** 132–43

[15] Soto D, Infante-Amate J, Guzmán G I, Cid A, Aguilera E, García R and González De Molina M 2016 The social metabolism of biomass in spain, 1900-2008: From food to feed-oriented changes in the agro-ecosystems *Ecol. Econ.* **128** 130–8

[16] Gingrich S and Krausmann F 2018 At the core of the socio-ecological transition: Agroecosystem energy fluxes in austria 1830-2010 *Sci. Total Environ.* **645** 119–29

[17] Debonne N, Bürgi M, Diogo V, Helfenstein J, Herzog F, Levers C, Mohr F, Swart R and Verburg P 2022 The geography of megatrends affecting european agriculture *Glob. Environ. Change* **75** 102551

[18] Peyraud J-L, Taboada M and Delaby L 2014 Integrated crop and livestock systems in western europe and south america: a review *Eur. J. Agron.* **57** 31–42

[19] Rodríguez A, Sanz-Cobeña A, Ruiz-Ramos M, Aguilera E, Quemada M, Billen G, Garnier J and Lassaletta L 2023 Nesting nitrogen budgets through spatial and system scales in the spanish agro-food system over 26 years *Sci. Total Environ.* **892** 164467

[20] Häußermann U, Klement L, Breuer L, Ullrich A, Wechsung G and Bach M 2020 Nitrogen soil surface budgets for districts ingermany 1995 to 2017 *Environ. Sci. Europe* **32** 109

[21] Britz W 2008 EU-wide spatial down-scaling of results of regional economic models to analyze environmental impacts Paper presented at the 107th EAAE Seminar, Sevilla, Spain (https://doi.org/10.22004/ag.econ.6677)

[22] Billen G, Lassaletta L and Garnier J 2014 A biogeochemical view of the global agro-food system: nitrogen flows associated with protein production, consumption and trade *Glob. Food Secur.* **3** 209–19

[23] Velthof G, Oudendag D, Witzke H, Asman W, Klimont Z and Oenema O 2009 Integrated assessment of nitrogen losses from agriculture in EU-27 using MITERRA-EUROPE *J. Environ. Quality* **38** 402–17

[24] Bouwman A, Kram T and Klein Goldewijk K 2006 *Integrated Modelling of Global Environmental Change: an Overview of Image 2.4* (Netherlands Environmental Assessment Agency (MNP))

[25] de Vries W, Leip A, Reinds G J, Kros J, Lesschen J P and Bouwman A F 2011 Comparison of land nitrogen budgets for european agriculture by various modeling approaches *Environ. Pollut.* **159** 3254–68

[26] Billen G, Aguilera E, Einarsson R, Garnier J, Gingrich S, Grizzetti B, Lassaletta L, Noë J L and Sanz-Cobena A 2021 Reshaping the european agro-food system and closing its nitrogen cycle: The potential of combining dietary change, agroecology and circularity *One Earth* **4** 839–50

[27] Batool M, Sarrazin F J, Attinger S, Basu N B, Van Meter K and Kumar R 2022 Long-term annual soil nitrogen surplus across europe (1850-2019) *Sci. Data* **9** 109

[28] Einarsson R, Sanz-Cobena A, Aguilera E, Billen G, Garnier J, Van Grinsven H J M and Lassaletta L 2021 Crop production and nitrogen use in european cropland and grassland 1961–2019 *Sci. Data* **8** 288

[29] Guyomard H and Mahé L-P 1995 Le GATT et la nouvelle politique agricole commune: Une réforme inachevée *Revue économique* **46** 657

[30] Giuliani A and Baron H 2025 The CAP (Common Agricultural Policy): a short history of crises and major transformations of european agriculture *Forum Soc. Econ.* **54** 68–94

[31] Grešlová P, Gingrich S, Krausmann F, Chromý P and Jančák V 2015 Social metabolism of czech agriculture in the period 1830-2010 *AUC Geographica* **50** 23–35

[32] Garnier J et al 2015 Phosphorus budget in the water-agro-food system at nested scales in two contrasted regions of

the world (ASEAN-8 and EU-27) *Glob. Biogeochem. Cycles* **29** 1348–68

[33] Wang Q, Barré P, Deng O, Lan T, Zeng M, Gao X and Noë J L 2025 Soil carbon sequestration, climate change mitigation, nitrogen pollution and agro-food supply: navigating trade-offs in future cropland management strategies *Environ. Res. Lett.* **20** 044004

[34] Buclet N 2020 *Territorial Ecology and Socioecological Transition* (ISTE Ltd./Wiley) (https://doi.org/10.1002/9781119821373)

[35] Lassaletta L, Billen G, Grizzetti B, Anglade J and Garnier J 2014 50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland *Environ. Res. Lett.* **9** 105011

[36] Hou Y, Bai Z, Lesschen J P, Staritsky I G, Sikirica N, Ma L, Velthof G L and Oenema O 2016 Feed use and nitrogen excretion of livestock in EU-27 *Agric. Ecosyst. Environ.* **218** 232–44

[37] Anglade J, Billen G and Garnier J 2015 Relationships for estimating N₂ fixation in legumes: incidence for N balance of legume-based cropping systems in Europe *Ecosphere* **6** art37

[38] Simpson D et al 2012 The EMEP MSC-W chemical transport model–technical description *Atmos. Chem. Phys.* **12** 7825–65

[39] Meyfroidt P and Lambin E F 2011 Global forest transition: prospects for an end to deforestation *Annu. Rev. Environ. Resources* **36** 343–71

[40] Kuemmerle T, Kaplan J O, Prishchepov A V, Rylsky I, Chaskovskyy O, Tikunov V S and Müller D 2015 Forest transitions in Eastern Europe and their effects on carbon budgets *Glob. Change Biol.* **21** 3049–61

[41] Kuhn T, Kokemohr L and Holm-Müller K 2018 A life cycle assessment of liquid pig manure transport in line with EU regulations: a case study from Germany *J. Environ. Manage.* **217** 456–67

[42] Oenema O, Kros H and de Vries W 2003 Approaches and uncertainties in nutrient budgets: implications for nutrient management and environmental policies *Eur. J. Agron.* **20** 3–16

[43] de Wit C T 1992 Resource use efficiency in agriculture *Agric. Syst.* **40** 125–51

[44] Laidig F, Piepho H-P, Drobek T and Meyer U 2014 Genetic and non-genetic long-term trends of 12 different crops in german official variety performance trials and on-farm yield trends *Theor. Appl. Genet.* **127** 2599–617

[45] Senapati N and Semenov M A 2020 Large genetic yield potential and genetic yield gap estimated for wheat in Europe *Glob. Food Secur* **24** 100340

[46] Lassaletta L et al 2021 Nitrogen dynamics in cropping systems under Mediterranean climate: a systemic analysis *Environ. Res. Lett.* **16** 073002

[47] Hopewell K 2015 Different paths to power: the rise of Brazil, India and China at the World Trade Organization *Rev. Int. Political Econ.* **22** 311–38

[48] Niavis S and Vlontzos G 2019 Seeking for convergence in the agricultural sector performance under the changes of uruguay round and 1992 CAP reform *Sustainability* **11** 4006

[49] Lemaire G, Franzluebbers A, Carvalho P C d F and Dedieu B 2014 Integrated crop-livestock systems: strategies to achieve synergy between agricultural production and environmental quality *Agric. Ecosyst. Environ.* **190** 4–8

[50] Raffray M 2025 Trump saison 2: quel scénario pour l'agriculture? *Paysans Societe* **409** 5–11

[51] Pouch T and Raffray M 2025 Mondialisation, guerre et fin de cycle hégémonique agricole et alimentaire *Actuel Marx* **77** 190–208

[52] Harter L, Einarsson R, Lata J C and Le Noë J 2025 DataSuds Territorial nitrogen budget and metabolic typologies of European agri-food systems, 1990-2019 (https://doi.org/10.23708/4FAPMG)