Robustness to import declines of three types of European farming systems assessed with a dynamic nitrogen flow model

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Practices or composition changes to decrease vulnerabilities

In the scientific literature, a certain number of changes in practices or compositions are identified and implemented in reality to reduce the vulnerability of European farming systems to the challenges they face. They aim to promote wild or cultivated biodiversity, reduce the use of inputs (water, pesticides, fertilisers and fuel), reduce GHG emissions, increase organic carbon stocks in agricultural soils, adapting cultivated plants and livestock to the consequences of climate change, reduce economic and social inequalities and improve the accounting of agricultural farms. Changes in practices concern, for example, herd management, genetic selection, effluent management, crop fertilisation and food waste. Changes in composition are understood to be all changes in crop area, livestock number and livestock diet. Changes in land cover (e.g. permanent grassland, cropland) are considered as structural changes. In a non-exhaustive way, I list below five actions, specifying the issues they address or the vulnerabilities they reduce, i.e. why they are implemented, and to what extent they are and/or can be implemented:
Re-establish hedgerows around plots to both host biodiversity (Pointereau, 2002) and store carbon in the soil (Pellerin et al., 2020). Nevertheless, for example in France, the trend towards a decrease in the number of kilometres of hedgerows is not currently being reversed (Solagro, 2017).
Increase the nitrogen use efficiency of both livestock at the individual level, for example, by opti-mising their diets (Millet et al., 2018), and crops, by optimising the application of nitrogen fertilisers at plot level. This increase would reduce the use of synthetic or organic fertilisers (Bouraoui et al., 2014) as well as GHG emissions (Gu et al., 2017). For crops, since the 2000s, the nitrogen use efficiency in France has changed little and is currently about 70%. However, the possibility of in-creasing efficiency is limited if we wish to avoid destocking soil nitrogen (Hutchings et al., 2020; Lassaletta et al., 2014a). For livestock, the nitrogen use efficiency in Northern Europe is between 25% and 35% (Hutchings et al., 2020). The possibility of increasing efficiency is also limited (at most +10%) but more important for monogastrics than for ruminants (Hutchings et al., 2020).
Increase recycling rates of human and animal excreta to reduce the use of synthetic fertilisers and GHG emissions (Peyraud et al. 2014). For human excreta, the recycling rate is low today in Europe. For example, for a city like Paris, the recycling rate of nitrogen from human excreta has gone from
50% at the beginning of the 20th century to less than 10% today (Esculier et al., 2019). The recycling rate of human excreta could be increased by composting organic waste, applying 100% of sewage sludge to agricultural land or separating and collecting urine at source (Esculier et al., 2019). For animal excreta, the recycling rate of nitrogen from livestock effluents was 75% before spreading in 2010 in France (Service de l’observation et des statistiques, 2013). This recycling rate could be increased by technical innovations along the management chain that would also make it possible to significantly reduce the content of undesirable elements, particularly chemical or medicinal residues (Peyraud et al. 2014).
Diversify crop rotations by adding legumes or adopting legume or legume-associated intercropping practices to reduce the use of synthetic fertilisers and improve soil quality and health (Nemecek et al., 2008; Schaller, 2012; Voisin et al., 2014). Nevertheless, today, for example, in France, the trend is still towards simplifying rotations (Schaller, 2012). Concerning associated crops, the pea-cereal mixture represented less than 100,000 ha (i.e. less than 0.3% of the agricultural area) in 2012 in France (Schneider and Huyghe, 2015). Before the 1950s, cereal-legume associations were never-theless very common in France (Schneider and Huyghe, 2015). The systemisation of intermediate crops would also have a significant effect on reducing the climatic impact of French agriculture (Solagro, 2017).
Integrate livestock (composition and diets) into cereal systems while decreasing feed-food compe-tition for biomass use to reduce feed imports (Billen et al., 2021a) or reduce the need for agricultural land (Van Zanten et al., 2018). Today, the trend is still towards stagnation in livestock size (Eurostat, 2020) and the content of concentrates in their diet is now several tens of percent for cattle and more particularly for dairy cattle (Hou et al., 2016). The livestock population in Europe should at least be halved to significantly reduce feed imports (Poux and Aubert, 2018).

Scientific challenge: assessing European farming systems resilience

As we have seen, there are numerous changes in practices and in the composition of European farming systems to reduce their vulnerability to the challenges they face. The scientific challenge is then to evaluate and therefore measure the effectiveness of these actions to reduce vulnerabilities. The concept of resilience is one of the measures that make it possible to evaluate the effectiveness of actions to reduce vulnerability (Prosperi et al., 2016; Urruty et al., 2016). This measurement requires the collection and statistical analysis of qualitative and/or quantitative data and can be also done through modelling (quantitative method) which consists of the mathematical description of the relationships between the spatiotemporal variables of the studied system. The interests of modelling are to be able to evaluate the future spatiotemporal evolution of the system’s functions in the face of one or more hitherto unobserved disturbances or simply to overcome a lack of historical data.

Resilience concept in the scientific literature

Today, in the scientific literature, the concept of resilience is used in many disciplines and applied to many systems. It was first used in materials physics at the end of the 19th century and later in psychology and computer science, before being used in agronomy, among other fields (Martin, 2015). The common idea of resilience is the capacity of the system to maintain or recover its properties despite disturbances over which it has no control. This capacity is often divided into components to facilitate its measurement (Meuwissen et al., 2019; Tendall et al., 2015). For example, Tendall et al. (2015) identify 4 components (Figure I.7): (i) the capacity to withstand disturbances, (ii) the capacity to absorb disturbances, (iii) the capacity to recover essential functions as quickly as possible (engineering resilience) and (iv) the capacity to adapt or transform. Finally, Carpenter et al. (2001) emphasised the importance of specifying which properties or functions of the system (resilience ‘of what’) and which disturbances (resilience ‘to what’) are being considered.

Assessment indices of resilience

Based on the components of resilience, depending on the quantitative approach adopted and the formalism used to describe the dynamics of the system in the context of modelling, i.e. its functions or properties, many measurement indices have been proposed. For example, in the case of dynamic macro-models with stable equilibria (or basins of attraction), it can be the amount of disturbance that a system can absorb without undergoing a jump in state into another basin of attraction (e.g. Beddington et al., 1976). The resilience measure can be the inverse of the time required after a shock to return to a state close to that before the disturbance in the case of agent based dynamic models (par exemple Ortiz and Wolff, 2002). In the case of controlled dynamic macro-models (in the form of differential equations), it can be the inverse of the cost of returning after a shock to a state from which the property can again be maintained (Martin, 2004). The latter two measures could also be used in the analysis of quantitative historical data. In the case of statistical data analysis, the resilience measure can be spatial and/or temporal statistical indices calculated from spatio-temporal data (Kahiluoto et al., 2019; Seekell et al., 2017) and be based not on resilience components but on resilience criteria (or attributes) (Cabell and Oelofse, 2012; Meuwissen et al., 2019; Resilience Alliance, 2010). For example, the Resilience Alliance (2010) proposed 4 generic resilience criteria: (1) diversity in responses to disturbances but also in functions; (2) modularity and connectivity, i.e. the internal division of the system into independent but connected modules; (3) cohesion of actors, which favours their collaboration and involvement; (4) autonomy of the territory in decision-making, food production, inputs etc.
The concept of resilience has also been increasingly applied in recent years to farming systems (or systems that encompass it2 ) in the scientific literature, resulting in numerous definitions (Douxchamps et al., 2017; Stone and Rahimifard, 2018). For example, Tendall et al. (2015) have defined the resilience of a food system as its capacity, and that of its components, to provide sufficient, appropriate and accessible food for all, over time in the face of various and unexpected disturbances, thus linking to the concept of food security (Comité de la sécurité alimentaire mondiale, 2012). Meuwissen et al. (2019), in their view, the resilience of a farming system is defined as its ability to perform its functions in the face of increasingly complex and cumulative economic, social, environmental and institutional shocks and constraints, through its capacity (i.e. components) for robustness, adaptability and transformation.

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Table of contents :

I General introduction
I.1 Global and societal context – An European agriculture vulnerable to environmental, energetic and economic challenges
I.1.1 The birth of an industrialised European agriculture
I.1.2 Environmental and social impacts of the European agriculture: source of global challenges
I.1.3 Trade-offs or synergies between functions in the industrialised European agriculture
I.1.4 Vulnerabilities of the industrialised European agriculture to the global challenges
I.1.5 Practices or composition changes to decrease vulnerabilities
I.2 Scientific challenge: assessing European farming systems resilience
I.2.1 Resilience concept in the scientific literature
I.2.2 Assessment indices of resilience
I.2.3 Resilience concept in the farming system literature
I.2.4 Quantitative resilience assessment in the farming system literature
I.3 Research question, general method and aims of the thesis
II General methodology
II.1 Why this type of model?
II.2 Modes of analysis used
II.3 Model description – Version of Chapter 2
II.3.1 General description
II.3.2 Description per compartment
II.3.3 Parameter sources and variable initialization
II.4 Scenarios simulated or optimised
II.5 Case studies
II.5.1 Three French farming systems
II.5.2 Grass-based beef cattle farming system in Alentejo, Portugal
Chapter 1 Robustness to import declines of three types of European farming systems assessed with a dynamic nitrogen flow model
1.1 Introduction
1.2 Material and Methods
1.2.1 General model description
1.2.2 Mathematical description by compartment
1.2.3 Simulated scenarios
1.2.4 Case studies
1.2.5 Parameters
1.3 Results
1.3.1 Extensive ruminant FS results
1.3.2 Intensive monogastric FS results
1.3.3 Field crops FS results
1.4 Discussion
1.4.1 Interaction of crop-grassland-livestock composition affecting robustness
1.4.2 Interaction between productivity and robustness
1.4.3 Decisive capacity of robustness for adaptability and transformability
1.4.4 Study and model limits
1.5 Conclusion
Chapter 2 European agriculture’s robustness to input import declines: a French case study
2.1 Introduction
2.2 Material and Methods
2.2.1 General model description
2.2.2 Scenario and simulations
2.2.3 Indicators of robustness to input import availability declines
2.2.4 Clustering indicators
2.2.5 Compositional indicators
2.2.6 Correlation matrix
2.2.7 Input data
2.3 Results
2.3.1 Robustness indicators
2.3.2 Relationship between robustness clusters and compositional indicators
2.3.3 Cluster description
2.4 Discussion
2.4.1 Crop-grassland-livestock compositions associated with robustness and input imports
2.4.2 Biological N fixation and robustness levels
2.4.3 Specialization of French FSs in the second half of the 20th century
2.4.4 Protein self-sufficiency under threat in 2050
2.4.5 Strategies for improving robustness: de-specializing agricultural regions?
2.4.6 Study limitations
2.4.7 Quality of estimates
2.5 Conclusion
Chapter 3 Crop-livestock compositional changes maximising food while minimising synthetic fertilizer use and feed import for three French farming system types
3.1 Introduction
3.2 Material and Methods
3.2.1 General model description
3.2.2 Optimization scenarios, driving variables and constraints
3.2.3 Case studies and input data
3.2.4 Optimisation procedure
3.3 Results
3.3.1 Pareto frontiers
3.3.2 Selected solutions
3.4 Discussion
3.4.1 Trade-off intensities explained by crop composition
3.4.2 MaxFoodMinImp solutions: increasing the monogastric number can optimise all the objectives
3.4.3 MaxFoodNoImp solutions: Without inputs, lower feed-food competition maximises food production
3.4.4 Legumes: marginal impact due to the current limited area
3.4.5 Resilience considerations regarding the implementation of compositional changes
3.4.6 Study limits and research perspectives
3.5 Conclusion
Chapter 4 Robust strategies for future meat production and climate change mitigation under imported input constraints in Alentejo, Portugal
4.1 Introduction
4.2 Materials and methods
4.2.1 Model overview
4.2.2 Description of model and adaptations
4.2.3 Simulations
4.3 Results and Discussion
4.3.1 Initial feed self-sufficiency
4.3.2 Temporal dynamics of meat production
4.3.3 GHG emissions
4.3.4 Total GHG balance versus meat production robustness
4.3.5 Implications of and limitations for the implementation of practice changes
4.3.6 Study and model limitations
4.3.7 Future research perspectives
4.4 Conclusion
III General discussion
III.1 Contributions
III.1.1 Major findings of the thesis
III.1.2 Collateral contributions
III.1.3 Methodological contributions
III.2 Methodological reflections
III.2.1 Reflections on the potential for conflicting conclusions
III.2.2 Reflections on the simulated disturbance scenario
III.2.3 Reflections on the food production objective
III.2.4 Reflections on model uncertainties
III.3 Research and applications perspectives
III.3.1 Research perspectives without conceptual development
III.3.2 Research perspectives with conceptual development
III.3.3 Applications perspectives
IV General conclusion

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