Diets influence dependency on synthetic nitrogen fertilizers

Diets influence dependency on synthetic nitrogen fertilizers | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Article Diets influence dependency on synthetic nitrogen fertilizers Wolfram Simon, Hannah Van Zanten, Renske Hijbeek This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5101296/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Europe’s nitrogen (N) crisis demands innovative food systems solutions to improve N cycling. This study modelled the potential of different diets and circular fertilization strategies to enhance food system N use efficiency (NUE), reduce N surplus, and minimize reliance on synthetic N fertilizers. Results show that circularity helps to improve NUE and total N losses but does not consistently improve N surplus per ha. Synthetic N fertilizer could be reduced by 95% if healthy diets were consumed in circular food systems, increasing NUE from the current 0.17 to 0.53. The reduction of synthetic N fertilizer led to increased use of manure and showed considerable trade-offs with land use and greenhouse gas emissions (GHGe). In contrast, circular systems in which vegan diets were consumed showed the lowest land use and GHGe and a relatively high NUE (~ 0.3). This emphasizes the importance of considering trade-offs and synergies between different environmental impacts when redesigning food systems. Earth and environmental sciences/Environmental sciences Biological sciences/Plant sciences Physical sciences/Chemistry/Environmental chemistry/Environmental monitoring Scientific community and society/Agriculture Social science/Environmental studies Figures Figure 1 Figure 2 Introduction Europe currently faces a nitrogen (N) crisis. Excessive N losses from agri-food systems have led to environmental challenges such as water pollution and a decline in biodiversity 1 . Innovative solutions are needed to improve nitrogen use efficiency (NUE) at the food system level, thereby reducing N losses. Especially in Western European countries, where N surplus can reach up to 171 kg/ha 2 . Transitioning towards a circular food system and a more plant-based diet might be a key solution that improves nutrient cycling and reduce reliance on external N inputs 3 – 5 . In a circular food system, biomass losses are prevented or otherwise recovered 5 . Transitioning towards food system circularity implies searching for practices and technologies that minimize the input of external and finite resources (e.g., synthetic N fertilizer and land), encourage the use of regenerative resources (e.g., organic fertilizers), prevent leakage of valuable resources (e.g., N, phosphorus (P)), and stimulate reuse/recycling of inevitable resource losses (e.g., human excreta) in ways that reduce the environmental impact in the most optimal way 5 – 7 . Previous research has demonstrated that circularity can effectively reduce greenhouse gas emissions (GHGe) and land use 8 , 9 . However, no study has yet assessed the potential of circularity to improve nutrient cycling and reduce external nutrient inputs at a whole food system level using a food system modelling approach. This study aims to assess how redesigning the food system based on circularity improves N cycling. Here, we will focus on N. Nitrogen is often the most limiting nutrient for crop growth, while excessive application has large environmental consequences 10 . More precisely, we assess the potential to increase NUE, reduce N surplus, and reduce reliance on synthetic N fertilizer in circular food systems under different dietary scenarios while accounting for potential land use and GHGe trade-offs. Our results show that a circular European food system improves NUE but still depends on synthetic N fertilizer. Results Scenarios Ten scenarios were assessed - one baseline and nine circular fertilization strategies - using the Circular Food Systems Model (CiFoS) 8 . CiFoS is a linear programming optimization model designed to assess pathways to minimize environmental impacts on a food system level. The baseline approximates the current amount of N sourced from manure in the food system 11 . Furthermore, it matches the current application of synthetic N fertilizer 12 , current protein intake 13 , current agricultural land use 14 , and current food waste 15 . The nine fertilizer scenarios differ in their objective function (minimizing land use, GHGe or synthetic N fertilizer) and dietary scenario (a current, healthy or vegan diet). The current diet (“Current”) is based on the current FAO protein intake 13 . The healthy diet (“Health”) fulfils the EAT-Lancet diet ranges per food group and macro- and micronutrient requirements 16 . The vegan diet contains no animal-based food and is supplemented with vitamin B12 and omega-3 fatty acids (DHA) (Table 1). We assessed NUE, N surplus, synthetic fertilizer N use, GHGe and LU for each scenario. Table 1 . Ten different scenarios per three diets and three objective functions. Nitrogen cycling Nitrogen cycling is assessed by five indicators: synthetic N fertilizer use (per ha and total), NUE, and N surplus (per ha and total). Below, we first show the potential to reduce synthetic N fertilizers on a food system level, after which we show the consequences for NUE and N surplus. Table 2 Performance on nitrogen cycling, land use and greenhouse gas emissions per scenario. Percentage values in brackets (%) show the relative deviation from the baseline scenario ‘BaseFert. ‘Synthetic N Fert(kg/ha)’ and ‘N surplus(kg/ha)’ in the scenario ‘BaseFert’ are calculated for the fertilized area of 134 Mha 17 . Scenarios Synthetic N Fert. (kg/ha) Synthetic N Fert. (Mt) N Use Efficiency (O/I) N Surplus (kg/ha) N Surplus (Mt) Land Use (Mha) GHGe (kg/cap/yr) BaseFert 83 11.1 0.17 58.4 10 172 1986 CurrentLU 216 (+ 160%) 9.5 (-14%) 0.24 (+ 41%) 182.4 (+ 212%) 8 (-20%) 44 (-74%) 1206 (-39%) CurrentGHG 104 (+ 25%) 11.7 (+ 5%) 0.15 (-12%) 105.4 (+ 80%) 12 (+ 20%) 112 (-35%) 1012 (-49%) CurrentFert 27 (-67%) 3.7 (-67%) 0.34 (+ 100%) 27.5 (-53%) 4 (-60%) 139 (-19%) 1800 (-9%) HealthLU 166 (+ 100%) 6.8 (-39%) 0.2 (+ 18%) 145.8 (+ 150%) 6 (-40%) 41 (-76%) 1050 (-47%) HealthGHG 77 (-7%) 5.6 (-50%) 0.18 (+ 6%) 75.6 (+ 29%) 6 (-40%) 73 (-58%) 427 (-78%) HealthFert 4 (-95%) 0.5 (-95%) 0.53 (+ 212%) 8 (-86%) 1 (-90%) 138 (-20%) 1678 (-16%) VeganLU 282 (+ 240%) 6.2 (-44%) 0.27 (+ 59%) 220.2 (+ 277%) 5 (-50%) 22 (-87%) 429 (-78%) VeganGHG 100 (+ 20%) 4.3 (-61%) 0.33 (+ 94%) 75 (+ 28%) 3 (-70%) 43 (-75%) 113 (-94%) VeganFert 95 (+ 14%) 3.9 (-65%) 0.34 (+ 100%) 71.9 (+ 23%) 3 (-70%) 41 (-76%) 416 (-79%) Potential to reduce synthetic nitrogen fertilizers on a food system level The total amount of N applied is reduced in all the scenarios compared to the BaseFert scenario (Fig. 1 A). Furthermore, the total amount of synthetic N fertilizer is largely reduced across all scenarios. Currently, 48% of all N applied in Europe comes from synthetic N fertilizers, which equates to 11.1 Mt of N (Fig. 1 A). This amount of synthetic fertilizer can be reduced to 3.7 Mt of N (67% reduction) in CurrentFert, 0.5 Mt of N (95% reduction) in HealthFert and 3.9 Mt of N (65% reduction) in VeganFert (Fig. 1 A-B, Table 2 ). When looking at the amount of synthetic N fertilizer applied per ha, our study shows that when minimizing synthetic fertilizer, the application rates can be changed from the current 83 kg N/ha in the BaseFert scenario to 27 kg N/ha (reduced by 67%), 4 kg N/ha (reduced by 95%) and 95 kg N/ha (increased by 14%) in the CurrentFert, HealthFert and VeganFert scenarios, respectively (Table 2 ). The increased application rate in the VeganFert scenario is mainly due to the reduced land use from 172 Mha in the BaseFert scenario to 41 Mha (reduced by 76%) (Fig. 1 A, Table 2 ). These findings show considerable potential to reduce synthetic N inputs in the food system. However, synthetic N fertilizers were never entirely removed, and most scenarios included a substantial share of synthetic N fertilizer application, especially in the vegan scenarios (Fig. 1 B). Composition of the fertilizer mix to reduce synthetic nitrogen fertilizer use The most substantial relative reductions in synthetic N fertilizer use (from 48% in the BaseFert scenario to 19% and 4% in the CurrentFert and HealthFert scenarios) are mainly achieved by increasing the amount of animal manure in combination with biological N fixation (Fig. 1 B). The relative share of animal manure in the fertilizer mix for crops and grasslands increased from 25% in the BaseFert scenario to 53% and 68% in CurrentFert and HealthFert, respectively. The animals in the HealthFert scenario were increasingly fed with biomass losses (e.g. slaughter waste) and leguminous forage crops (e.g. clover), as well as highly nutritious concentrate crops (e.g. wheat). This strategy increased animal N intake and thus N excretion to maximize N from manure in the food system. Biological N fixation changed from providing 4% of total N applied in the BaseFert scenario to 10% and 12% in the CurrentFert and HealthFert scenarios (Fig. 1 B). In both scenarios, N from manure and biological N fixation increases in relative and absolute terms (Fig. 1 A, B). Without access to animal manure, the VeganFert scenario reduces synthetic N fertilizer use (compared to BaseFert) by increasing the use of food waste as fertilizer (from 5–16%), increasing biological N fixation (from 4–8%), and increasing N from human excreta (from 1–6% of total N applied). The share of N from crop residues stayed relatively high and constant across all scenarios (providing 11–16% of all N input to crops and grassland; Fig. 1 B). We further found that minimizing synthetic fertilizer does not directly mean minimizing the overall amount of N applied in the food system, as can be seen when comparing the total N applied in HealthGHG to HealthFert (11 vs 15 Mt N; Fig. 1 A).HealthFert has a much lower synthetic fertilizer N application than HealthGHG (0.5 vs 5.6 Mt N; Table 2 ). This finding indicates trade-offs between minimizing synthetic N and other environmental impacts on a food system level. Consequences for nitrogen use efficiency and nitrogen surplus In the baseline scenario (BaseFert), the food system’s NUE is 0.17, and the average N surplus is 58 kg N/ha, or 10 Mt N losses in total (Table 2 ). In most scenarios, NUE and total N losses are improved, but only in CurrentFert and HealthFert does the N surplus per ha decrease (Table 2 ). The largest NUE and N surplus improvements are achieved when the model’s objective function is to reduce synthetic N fertilizer use. When synthetic N fertilizer is minimized with the current diet, NUE is 0.34, and N surplus is 27 kg N/ha, resulting in total losses of 4 Mt N. With healthy diets, NUE would be 0.53 and N surplus 8kg N/ha or 1 Mt N in total. With a vegan diet, NUE would be 0.34 and N surplus 72 kg N/ha or 3Mt N in total. Therefore, the highest NUE and lowest N losses are achieved by changing towards healthier diets with fewer animal-sourced proteins (ASP) (HealthFert scenarios). It is, however, essential to note that even under current consumption patterns, NUE and N surplus can vastly be improved. Trade-off and synergies with land use and greenhouse gas emissions We found that reducing synthetic N fertilizers in the food system can lead to land use and GHGe trade-offs. Land use The current agricultural land use is 172 Mha (BaseFert scenario) (including crop and temporary and permanent grassland). Compared to the BaseFert, all scenarios reduced land use, ranging from 19% (to 139 Mha) in the CurrentFert scenario to 87% (to 22 Mha) in the VeganLU scenario (Table 2 ). The most considerable reductions are achieved when the model’s objective function is to reduce land use. For different diets and compared to BaseFert, land use was reduced by 74% for CurrentLU, 76% for HealthLU, and 87% for VeganLU. Vegan diets consistently resulted in the lowest land use, regardless of the model’s objective function (land use, GHGe, or minimizing synthetic fertilizer) (Table 2 ). Compared to the “Fert” scenarios, all nitrogen cycling indicators decreased in their performance when the objective function of the model was to minimize land use. Synthetic N fertilizer increased with 5.8Mt with a current diet, 6.3Mt with a healthy diet and 2.3 Mt with a vegan diet. The highest NUE is achieved with a vegan diet (0.27 for VeganLU). Nitrogen surplus shows more varied results, as total N surplus is also lower with a vegan diet (5 Mt N). However, this is not the case per ha (VeganLU has an N surplus of 220 kg N/ha, Table 2 ). Greenhouse gas emissions The EU food system currently emits around 1986 kg CO2eq/cap/year, originating from animal and crop production systems and transportation. Compared to BaseFert, all scenarios have reduced GHGe. Diets greatly impacted the results, ranging from 1800 to 113 kg CO2eq/cap/year. In the GHGe minimization scenarios, emissions could be reduced by 49% (1012 kg CO2eq/cap/year), 78% (427 kg CO2eq/cap/year) and 94% (113 kg CO2eq/cap/year) for the scenarios CurrentGHG, HealthGHG and VeganGHG, respectively, compared to the BaseFert scenario. The lowest emissions were found for the vegan diets, regardless of the objective function (113, 416, 429 kg CO2 eq/cap/year). Furthermore, healthier diets resulted in lower emissions compared to the current diet. When minimizing GHGe, synthetic N fertilizer use increased by 8Mt with a current diet, 5.1Mt with a healthy diet and 0.4 with a vegan diet, when minimizing GHGe compared to minimizing synthetic fertilizer N use. The increased use of synthetic fertilizer indicates that lower emissions can be achieved with a fertilizer mix containing more synthetic N. This is partly explained by the high amounts of manure which cause manure storage emissions, enteric fermentation emissions and manure application emissions, which overall seem to be higher compared to the production and application emissions of synthetic fertilizers. Environmental impact per diet The results for current diets show that the minimizing land use scenario has the most synergistic effect on all three impacts (Fig. 2 A-C), as overall impacts for land use, GHGe, and synthetic fertilizers were lowest (Fig. 2 C). The N fertilizer mix with the highest synergies at current diets (CurrentLU) came from 67% synthetic fertilizer, 13% crop residues, 7% manure, and 7% biological N fixation (Fig. 1 B). For healthy diets, minimizing land use and GHGe showed equally high synergies between land use, GHGe and synthetic N fertilizers (Fig. 2 E-F). The fertilizer mix of these two scenarios was also very similar in total N applied and share of different fertilizers. In the HealthLU and HealthGHG, respectively 57% and 52% came from synthetic fertilizers, 20% and 18% from animal manure, 12% and 13% from crop residue N, and 4% and 9% from biological N fixation (Fig. 2 B). The only real difference is thus in the lower N fixation in the HealthGHG scenario. This indicates the trade-off between land use and cultivating legumes as indirect N fertilization. The vegan diets were shown to have the lowest overall impacts across land use, GHGe, and amounts of synthetic fertilizer application. The scenario with the lowest overall environmental impact was VeganGHG, which relied mainly on synthetic fertilizer (66% of all N applied), crop residues (13%), food waste (7%), N fixation (7%) and human excreta (6%) (Fig. 1 B and Fig. 2 H). To conclude, minimizing land use and GHGe showed synergistic relationships with each other and similar fertilizer mixes. Reducing synthetic fertilizer alone is not a suitable objective, as it can have large trade-offs with land use and GHGe. The total amount of applied fertilizer N seems to be a bigger determining factor for environmental impact than the type of fertilizer used (synthetic or circular/organic fertilizer). These nuanced results demonstrate the importance of assessing fertilization at a food system level, linking production (including fertilization), consumption, and environmental impacts. Discussion Nitrogen use efficiency increases in circular food systems Our findings show that redesigning the food system based on circular principles can positively affect NUE. Most circular scenarios demonstrated an NUE of around 0.3 compared to 0.18 in the baseline. A study focusing on Western Europe also found a positive effect of circularity on NUE, stating that 55% of N in organic recoverable N streams is currently recovered, and that increased nutrient recycling could replace almost half of the external N input 18 . A study confirmed our baseline food system NUE of 0.18 precisely and stressed the importance of dietary shifts to reduce N losses on a food system level 19 . We found that NUE increased most when synthetic N fertilizers were minimized in the model. With healthy diets and minimizing synthetic N fertilizer, NUE can be increased from 0.18 in the baseline to a maximum of 0.53 in the HealthFert scenario. One reason for the highest NUE of 0.53 was the substantial amount of manure in this specific scenario (HealthFert). In this scenario, the model optimized the amount of nitrogen-rich manure produced from animals fed with mainly biomass losses of the food system and legumes. Nitrogen cycling was optimized, but this also led to considerable trade-offs with land use and GHGe. This strategy aligns with a study that found that dairy farms have the most considerable contribution to cycled flows (35% for N) and thus have the potential to increase NUE. The narrative of animals closing nutrient gaps matches the general circularity concept of animals as nutrient recyclers 3 . Our study finds a similar trend, but with trade-offs on other environmental impacts. In contrast, shifting towards vegan diets while reducing GHGe showed synergistic effects across all three impact categories (i.e. land use, GHGe and N cycling). In this case, NUE was increased slightly less (to 0.33) and total synthetic N fertilizer use was reduced (by 65%), but required much less land and GHGe (reduced by 76% and 79% compared to the baseline), only the kg synthetic fertilizer N per ha was increased in this case (by 14%). Several studies align with our findings by stating that more plant-based diets decrease N losses and increase NUE on a food system level 19 , 20 . In most scenarios, NUE and total N losses are improved. However, the N surplus per ha only decreases in CurrentFert and HealthFert, while current N surpluses often already exceed regional thresholds for N impacts in Europe 21 . Depending on the spatial allocation of agricultural fields across regions, this requires caution when implementing measures to decrease synthetic N fertilizer use. This also shows that strategies that only focus on reducing NUE on a food system level can put pressure on local ecosystems with high N losses per ha, which can negatively affect local biodiversity 22 . Including such spatially explicit burdens (e.g., biodiversity loss due to N losses into nature areas) in the CiFoS model could be explored in the future. Circular food systems depend on synthetic N fertilizers Our results show that all scenarios have some synthetic N fertilizers remaining in the food system. This goes against a widespread conception that food systems would be more environmentally sustainable without synthetic N fertilizer inputs. The most institutionalized example of this standpoint is the EU regulation for organic agriculture that legally prohibits synthetic fertilizers in organic agriculture 23 . Based on our results, synthetic fertilizers should be reduced but not eliminated. When using integrated soil fertility management strategies, combining synthetic N fertilizer with other organic and circular fertilizers can support sustainable soil management and increase soil organic matter levels 24 , 25 . As such, synthetic fertilizer could be part of a sustainable European food system strategy by filling the nutrient gaps that cannot be filled by circular fertilizers alone. Without synthetic fertilizers, there would be a real risk of soil mining over time 26 . The lever for a sustainable food system is not synthetic fertilizers but suboptimal management, which results in the risk of over- or under-fertilization and losses 27 . Fertilization in a plant-based food system Our results show that the vegan food systems showed the lowest total amount of N applied. When going plant-based, this reduction in N requirements aligns with a study stating that shifting towards more vegan diets can substantially reduce fertilizer use 19 . Compared to the other dietary scenarios, we found that vegan scenarios depend most on synthetic N fertilizers in relative terms (ranging from 57–77% of total N applied). So, despite the lower N use in the vegan system, in our study, it is only possible to generate and recycle sufficient N to feed the EU28 population with synthetic fertilizers. Many studies about vegan agriculture and fertilization do not align with these findings, as there is a firm conviction that no or little external inputs are needed 28 , 29 . Apart from synthetic N fertilizers, the central circular fertilization strategy in the vegan food system uses crop residues, N fixation, food waste (composted), and human excreta as fertilizers. In contrast to the literature, where cash and carry (in Fig. 1 referred to as GreenManureCrop) is often presented as the major fertilization strategy in vegan systems, it was not selected by any scenario in our research. Practical aspects of using organic fertilizers Even though we are assessing the potential of increased use of organic fertilizers in this study, we acknowledge that increased use of organic fertilizers presents several practical challenges that require careful management and infrastructure adjustments. To optimize utilization of nutrients from new organic and circular resources, more insights are needed on the composition, the plant-availability of nutrients, and the presence of contaminants 30 . One specific issue is managing the fixed nutrient ratios between N and other macro and micro nutrients the plants require. Organic fertilizers are often variable in their nutrient contents and how fast organically bound nutrients become plant-available. The unpredictability of nutrient contents makes synchronizing nutrient availability with crop demand more challenging 6 . Organic fertilizers are also relatively rich in phosphorus because N has a higher potential for loss. This imbalance between N and other nutrients makes it challenging to provide sufficient amounts of N without overfertilizing other, more available nutrients in a fully circular system 31 . Additionally, organic fertilizers can contain various pollutants, such as heavy metals and pathogens, which may pose environmental and health risks if not adequately treated and monitored 32 , 33 .Infrastructure facilitating organic fertilizers, such as human excreta or liquid manure, can pose a further challenge. Safely using human excreta requires reframing “waste” as a valuable resource and restructuring sanitation and sewage treatment systems to reduce health risks and improve nutrient recovery 34 . Moreover, transporting organic fertilizers can be challenging due to bulkiness and lower nutrient concentration (compared to synthetic fertilizers) 35 . These are just a few practical aspects of transitioning towards circular fertilizer that must be carefully considered when redesigning the nutrient cycles of food systems. Trade-offs The trade-offs between minimizing land use, GHGe, and synthetic N fertilizers are complex. Our study indicates that the most minor trade-offs occur between minimizing LU and GHGe. Minimizing synthetic N fertilizer often leads to increased land use and GHGe due to increased N-fixing legumes and intensified need for livestock and manure. This increased demand results from compensating for the reduced N availability from synthetic N fertilizers 36 . Vegan diets tend to achieve the lowest overall environmental impact, demonstrating synergies with reduced land use, GHGe and synthetic N fertilizer application 3 – 5 , 37 . This is because plant-based diets require less land and result in lower emissions per unit of food produced 38 . It is essential to note that reducing synthetic N fertilizers does not necessarily equate to a reduction in the total N applied within the food system, highlighting the need for integrated nutrient management strategies that are assessed with a diverse set of metrics, as was also done in this study. Thus, reducing the food system’s environmental impact requires a fertilization mix that balances the synergies and trade-offs between different environmental impacts. Online methods Circular food systems model This study is based on the European Circular Food Systems model (CiFoS), first developed by van Zanten et al. (2023) 8 and later extended by Simon et al., (2024) 9 . CiFoS is a biophysical food system optimization model written in the General Algebraic Modeling System (GAMS) that incorporates circular principles due to its unique model structure 39 . The model represents the food system components of food and diets (e.g., nutrition), production (e.g., animal and plant production system, fertilization) and processing (e.g. side-waste streams of organic by-products) and is developed for the EU28 context. The smallest unit of assessment is the agroecological-soil-climate zones for crops. All other components are optimized on a country level using a bottom-up approach 8 . The modelling work in this study extended the original model with new features regarding fertilization and N cycling. Most parts of the initial model, however, remained unchanged. In the following sections, I will give an overview of the different model components and data inputs used, explain food system N indicators, and end with a scenario description. Human nutrition For healthy diets, we ensure that CiFoS diets fulfil all nutrient requirements of up to 37 macro and micronutrients, according to the European Food Safety Authority (EFSA) 16 . Nutrients can be switched to design different dietary scenarios (e.g. vegan diets). Vitamin D and iodine requirements were consistently ignored in this study due to mandatory salt fortification for iodine in the EU and implicit limitations in obtaining enough vitamin D from diets alone. The nutritional data of the CiFoS food products is based on the FoodData Central Data from the US Department of Agriculture, Agricultural Research Service (USDA) 40 . We further included limiting food intake per product and family to healthy ranges based on the EAT-Lancet diet 41 . Apart from the reference scenarios, all scenarios complied with the EFSA nutrient requirements and the EAT-Lancet diet 41 .CiFoS includes over 150 food products that can be endogenously produced and processed to model health-optimized and sustainable diets from animal and plant sources. Dietary baseline scenarios are constrained to protein intake levels given by the Food Balance sheet from FAOSTAT 13 . The many food items and constraint options make CiFoS highly flexible when formulating dietary scenarios. Land use and land types The total agricultural land (i.e. crop and grassland) in the EU28 is 172 Mha 42 . CiFoS distinguishes three land types: arable (i.e. crops and arable grass), permanent managed grassland and unmanaged grassland (i.e. rangelands). Arable land can be cultivated with crops and temporary grassland in crop rotations. Arable land extends were derived from the ’IIASA-IFPRI cropland map 43 , while for grassland extends (managed and unmanaged), the pasture and rangeland datasets from the History Database of the Global Environment (HYDE) dataset was sourced 44 , 45 . The different grassland types were defined as follows: when the pasture dataset overlapped with the cropland, it was defined as temporary grassland. When pasture grassland did not overlap, it was considered permanent grassland (managed). Areas where the rangeland extended and did not overlap with the crop or pasture grassland area were considered rangeland. Land use changes such as deforestation have not been implemented in CiFoS. Therefore, expanding crops or grasslands can never lead to deforestation in our optimization runs. Cropping system CiFoS represents 43 food crops and eight fodder crops, including three grass types (temporary, permanent and rangeland). Yield and harvested area data come from the Global Spatially-Disaggregated Crop Production Statistics Data for the reference year 2010 (Version 2.0) (SPAM) 42 . Yield and harvested area for the fodder, tree nuts and vegetable (red, green and other vegetables) crops were taken from the EARTHSTAT dataset ‘Harvested Area and Yield for 175 Crops’ 46 . Crop production data sets represent current yield levels. Yields and area data were spatially extracted for agroecological climate-soil zones. These zones were created based on the intersection of the global agroecological zones 47 and the IPCC soil classes derived from the Harmonised World Soil Data Base 48 . All crops can be endogenously selected during runtime as area shares of climate-soil-zones. Crop rotations Crop rotation constraints are applied to all annual crops. Crop rotation is modelled as the years a crop needs between two cultivation events to avoid soil-borne diseases. This rotation break number allows the assignment of a specific maximum land share per zone for each crop. We thus convert a temporal crop rotation into spatial area shares, meaning that when a crop needs three years between cropping events, we allow this crop only one-third of the area. The crop rotation data is derived from Pahmeyer (2019) 49 . Crop nutrient requirements and losses Crop fertilization considers the nutrients N and phosphorus. In this paper, however, we only report on N. Fertilizer requirements per crop are defined as the sum of the total amount of above-ground nutrients in the plant and the nutrient losses from the application. This approach ensures a balanced fertilization that dynamically adjusts to yield levels (the reasoning of different N requirement methods is further explained in Simon et al. (2024a) 50 ). The loss fraction for P was 12.5% of the applied nutrients 51 . We calculate the application losses more dynamically for N based on the IPCC equations, which include direct and indirect N2O and run-off/leaching losses 52 . These N losses are climate-soil-type dependent and are calculated on a climate-soil zone level using the disaggregated emission factors from IPCC. N-fixation is directly subtracted from the amount of N in the crops. In the model, we allow for 20% overfertilization of nutrients but no underfertilization. Fertilizer types To meet the crop nutrient requirements on a climate soil zone level, CiFoS allows for fertilization with the following circular fertilizer types: food waste and losses (composted), animal manure from manure management systems and grazing, human excreta, crop residues, N fixation from cultivated crops, green manure from cover crops and main crops. Synthetic N and mineral P fertilizers are also allowed to fill the gap between the nutrients available from circular fertilizers and those required by crops. Nitrogen deposition is excluded in this study because it depends on the current food production system (especially on livestock systems). As we consider potential future scenarios, we aim to omit these regional N deposition inputs that rely on current production methods. Including these inputs would bias crop production towards areas with currently intense livestock farming and high N deposition (e.g. the Netherlands) due to the availability of “free” N input. Food losses and waste Nutrients from food losses and waste are calculated along all supply chain stages, including post-harvest, processing and packaging, distribution, retail, and consumption losses 53 . Post-harvest, processing and packaging, and distribution of waste are available as fertilizer in the country of production. To derive the N and P content in the losses, we use the N and P values from the CVB nutrition dataset 54 . Food losses and waste can be used as fertilizer and feed for farmed monogastric animals and fish to minimize food safety hazards. Animal manure All farmed animals (except farmed fish) in CiFoS produce manure. The amount of nutrients in the manure is a function of nutrients from feed intake and nutrient retention fractions in the animal. There are two manure types in CiFoS. Firstly, when ruminants graze, the excreted manure is considered nutrient input for the grasslands in the grazed soil climate zone. Secondly, when cows are kept indoors, manure enters a manure management system and can be applied anywhere in the country of production. Human excreta The CiFoS model has an exogenous and endogenous capability to model nutrients from human excreta. The exogenous method relies on sewage sludge data from EUROSTAT, which states that 36% of sludge produced in EU28 is applied on agricultural land 55 . We use this share only in the BaseFert scenario. In the baseline, we assume a dry matter (DM) content of 25% and a nutrient content for sludge of 7.5% and 1.2% of N and P, respectively 56 . For this study, we developed another endogenous approach to calculating the nutrient contents in human excreta based on the approach of van Drecht (2003) 57 . The nutrient input is derived from protein converted to N by a protein to N fraction of 0.16 58 . N can be converted to P using a N to P fraction of 0.17. Having established the N and P inputs in human food, we can calculate the nutrient outputs using an excretion fraction of 0.37 57,59 . The recovery rates were calculated using the ‘Sanitation technology library’ that provides recovery ratios for N and P per sanitation technology 60 . To calculate nutrients in urine from faeces, the conversion ratios of urine to faeces of (0.16/0.06) and (0.16/0.06) were used for N and P, respectively 61 . This paper distinguished between the following facility types: sewer systems, septic tanks, improved latrines, unimproved pits, and open defecation 60 . The share of the population using each facility type per country was sourced from a household sanitation data set with global coverage 62 . With all this information, we could endogenously calculate the N and P coming from human excreta and model them as fertilizer inputs for plant production systems. Crop residues Crops and grass produce crop residues. This fraction was calculated based on IPCC equations on estimating N in crop residue 63 . Only the above-ground residues were accounted for, which was everything but main harvested product. The nutrient content and DM of the residues came partly from IPCC and from the USDA nutrient tool 64 . The amount of residue was a function of harvested yield 63 . In our study, crop residues can only be applied as fertilizer locally in the same climate-soil zone in which the crop was produced. Nitrogen fixation from cultivated crops N fixation from cultivated crops is done for all the legume crops and in small amounts also for grass (grass clover mixture assumed). Biological Nfixation is calculated using the following formula following an adjusted approach from 65 : $$\:\begin{array}{r}Nfixation\left[kg/ha\right]=Ndfa/100\text{*}Y\end{array}$$ Where Ndfa is the percentage of N uptake derived from biological N fixation, Y is the total above ground yield including residues (expressed in kg N/ha/yr). Green manure from cover crops Cover crops in CiFoS can only be combined after winter sown crops (i.e., barley, other cereals, wheat and rapeseed) in temperate climate zones, on maximum 50% of the arable fields, as cover crops need an early harvest to allow for planting in autumn and a reasonable growing season. Cover crops are assumed to be legumes that fix 23 kg N/ha 66 . We further assume that only 50% of N will be plant-available 66 . Green manure from main crops Using the main crop as fertilizer follows a simple approach: All the nutrients in the harvested product are used as fertilizer for crops. Together with the residues, this implies that the whole crop is used as fertilizer. Synthetic N fertilizer and mineral P fertilizer Synthetic fertilizers can fill the gap between required and applied nutrients. N and P can be applied independently. Animal system Animal production systems encompass dairy, beef, pigs, broilers, and farmed fish (both freshwater and saltwater) 67 . We utilized production data for Nile tilapia and Atlantic salmon as representatives for freshwater and saltwater species, respectively. Livestock systems are categorized into three intensity levels, while farmed fish reflect current productivity standards. For each animal type and productivity level, we apply adjusted nutrient requirements. The model also accounts for the nutrient needs of the entire herd structure, including reproductive stock (e.g., heifers in dairy systems) and parent stock (e.g., sows in pig systems). Various feed ingredients can be chosen to meet these requirements, such as co-products, food waste, grass resources, animal by-products, and - if not otherwise utilized in the food system - high-quality biomass like grains. The feed ratio is determined as a model outcome. The animal-source food output from these production systems is influenced by three factors: the quantity and quality of biomass and grass resources available for farm animals, the efficiency of animals in converting biomass into animal-source food, and human nutrient requirements 67 . Capture fisheries Capture fisheries can be selected as an additional source of animal-based food for human consumption and animal feed (limited to by-products). The maximum landings of capture fisheries are obtained from the RAM Legacy Stock Assessment 68 and FAO marine capture data 69 . To prevent feed-food competition, we differentiate between the edible yield fractions of all landed fish and their by-products unsuitable for human consumption. Processing of food and feed Plant and animal-source food products are processed into primary products (e.g., wheat flour) and by-products (e.g., wheat bran). These processing fractions are primarily based on FAO’s technical conversion factor document 70 . A crop can yield multiple primary products (e.g., white wheat flour, whole wheat flour, wheat grains) and various by-products. Animal by-products represent a portion of the live weight output from each farmed animal system 67 . These by-products can be used as animal feed or fertilizer. Transportation Food, feed, and associated by-products can be transported between EU28 countries by lorry. However, food waste, manure, and grassland are restricted to usage within the country of origin. The underlying assumption is that crop and livestock products are processed into food, feed, and by-products within their country of origin before being transported to the destination country for consumption. The distances between countries are calculated using the centroids of each member state and the distances between them. Greenhouse gas emissions Greenhouse gas emissions from animal production system We utilized IPCC tier 2 methodologies to calculate GHGe 71 . The GHGe associated with farmed terrestrial animals (dairy, beef, pigs, broilers, and layers) included methane (CH4) and nitrous oxide (N2O) emissions from livestock manure management. Livestock manure management contributes significantly to CH4 and N2O emissions. To estimate methane emissions from manure management, we applied a formula that multiplies volatile solid excretion by the methane conversion factor (specific to each manure management system, or MMS), B0 (the maximum methane production potential of manure), and 0.67 (to convert methane from cubic meters to kilograms of CH4). Volatile solid excretion was calculated by considering the digestibility of protein and organic matter in the feed consumed by each animal species. The CiFoS model internally calculates the amount of animal feed consumed. For N2O, emissions from manure include both direct and indirect emissions, with indirect emissions resulting from the volatilization of ammonia and N2O. N excretion was calculated by subtracting the N retained in meat, milk, or eggs from the total N intake. N2O emissions were then estimated by multiplying N excretion by the appropriate emission factor, which varies according to species and the type of housing system used. In aquaculture systems, N2O emissions are considered within the system. Unconsumed feed and excreta containing N (calculated as the difference between N intake and N retained in body tissues) were multiplied by 1.8% and converted from N to N2O 48 , 72 . Additionally, we accounted for ruminant systems’ methane (CH4) emissions from enteric fermentation and N2O emissions from grassland fertilization. Methane emissions from enteric fermentation were calculated by multiplying gross energy intake by Ym (the proportion of gross energy in feed converted to CH4) and dividing by 55.65 (the gross energy content of methane). This method adheres to the IPCC tier 2 approach 48 . N2O emissions from grassland included both direct and indirect emissions, with indirect emissions arising from the volatilization of ammonia and N2O and nitrate leaching. These emissions are due to N fertilization and manure release during grazing 52 . Greenhouse gas emissions from cropping systems The cultivation of crops leads to the release of N2O and CO2 emissions. We utilized the IPCC Tier 1 methodology to estimate crop-related emissions 52 . For N fertilization of crops, factors such as fertilizer type, soil characteristics, and climate conditions influence N2O emissions. These emissions can be direct or indirect, with indirect emissions arising from ammonia volatilization and nitrate leaching into the environment. To estimate N2O emissions, we multiplied the applied fertilizer amount by the appropriate emission factor, which varies depending on the specific N fertilizer type and the climate-soil zone. We also included emissions related to the production of synthetic fertilizers using data from the ecoinvent database 73 . In our calculations, we considered N2O emissions from drained organic soils, taking into account factors such as land use, climate zone, and soil type (whether peat or non-peat) 52 , 63 , 74 . It is essential to mention that CH4 emissions from rice cultivation were not included, as they were deemed negligible in our analysis. Additionally, CO2 emissions from crop management activities (e.g., tractor fuel use) were not considered. Greenhouse gas emissions from compost The composting of food waste results in the release of N2O and CH4 emissions. To estimate N2O emissions, we used the N content in the food waste and applied a N loss fraction of 38%, converting the lost N into N2O. For CH4 emissions, we calculated them based on N losses by converting the N content in the compost into carbon, using the average compost’s carbon-to-nitrogen (C:N) ratio, which is ideally around 15. We then converted this carbon content into the total CH4 emissions 75 . Greenhouse gas emissions from transportation The transportation of crops, fish, food, by-products, manure, and food waste involves the combustion of fossil fuels, leading to CO2 emissions. We calculated the distances between countries by measuring from one country’s centre point to another’s centre point. Then, we determined the total ton-kilometres for this transportation. The ton-kilometres were multiplied by an emission factor from the Eco-invent database 73 . To estimate the total GHGe, we aggregated them into CO2eq, using a 100-year time horizon (GWP100). A factor of 28 was applied for CH4 and 265 for N2O. The results provided GHG emission totals for the EU28 as a whole and per capita per year 52 . Circular Principles Circularity presents a systemic solution by reducing unavoidable waste streams such as food waste and overconsumption of nutrients. If waste is unavoidable, waste streams are reused in the most sustainable manner possible. Additionally, food processing produces by-products such as wheat middlings during flour production. These by-products and waste streams can be used as compost to reduce the need for artificial fertilizers. Furthermore, if waste streams are used as feed for farmed animals, inedible biomass for humans could be transformed into livestock products and manure, leading to increased ecosystem services due to improved soil fertility and less pressure on land 6 , 76 . The fundamental principles of circular food systems are centred around avoiding and reusing waste and by-product streams to close biomass and nutrient cycles. By defining the healthy diet, we therefore also avoided overconsumption of proteins. In this study, circularity was modelled as follows. First, farmed animals can be fed by organic side streams 77 . Second, the edible ratio of animals is increased to avoid food waste, meaning that humans consume all edible parts of the farmed animals (i.e. offal), and overconsumption is avoided. Third, nutrient recycling is improved by fostering circular fertilization, such as using leguminous crops in crop rotation compost from organic waste streams and crop residues to reduce artificial fertilizer inputs. Scenario description Reference scenario BaseFert The reference scenario (BaseFert) aims to represent the current N fertilization mix in the EU28 food system. BaseFert has the objective function to minimize the difference to the current amount of N from animal manure in the food system 11 . Moreover, BaseFert matches the current application of synthetic N fertilizer on a EU28 level 12 , and the amount of N used as fertilizer from sewage sludge 55 . Diets and agricultural land use are fixed to the current protein intake level 13 and agricultural land use 14 , 78 . GHGe match current GHGe on a sector level, meaning for crops, animal and transport components 79 . Food can be imported to account for gaps in food supply. The nine fertilizer scenarios differ in their dietary scenario (a current, healthy or vegan diet) and objective function (minimizing land use, GHGe or synthetic N fertilizer). Diet scenarios All the scenarios had an upper bound for GHGe of 1800 kgCO2/cap/day, close to current emissions. In the optimized scenarios, all circular fertilizer options were allowed so that the model could optimize the fertilizer mix to improve the objective value. These fertilizers differed from the BaseFert scenario: human excreta (endogenous), compost, and green manure (cover crop and main crop). The current diet scenario (“Current”) was constrained to match the current FAO protein intake 13 but was relaxed in the other nutrient requirements. Offals were allowed to be used as food in the Current and the Healthy diets (Health). The Health diet fulfils the EAT-Lancet diet ranges per food group and macro- and micronutrient requirements 16 . However, the Health diet can freely optimize the amount of total protein, protein sources and the share between ASP and PSP. The vegan diet is completely plant-based and can thus not fulfil ASP derived nutrients such as vitamin B12 and omega-3 fatty acids (i.e., DHA). Therefore, we excluded these nutrients from the nutrient requirements (Table 1). Objective functions Each of the dietary scenarios was assessed with three objective functions: 1) minimizing land use, 2) minimizing GHGe, or 3) minimizing synthetic N fertilizer. By using one reference scenario, three different optimization approaches, we generated a total of 10 scenarios. Food system indicators - nitrogen use efficiency and surplus Nitrogen Use Efficiency (NUE) is defined here on a food system level as NUE = N output / N input. A food system’s output is food from plant and animal sources. Inputs, on the other hand, are mainly synthetic N fertilizers and N fixation. Nitrogen was fixed by the main cultivated crop (e.g., soybean) or the leguminous cover crops. These fertilizer sources were the input of nutrients into the food system. All the rest are recycled nutrients. N surplus is defined on a food system level as: N surplus = N input − N output. The input and output are the same as when calculating the NUE. Software and data analysis All data transformation, analysis and visualization was performed using R (version 4.2.2) 80 . The optimization modelling was performed using the General Algebraic Modeling System (GAMS) 39 . Declarations Acknowledgements This project received funding from the AVINA Foundation ( https://avinastiftung.ch/ ). 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5101296","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":368544819,"identity":"cf25d4b4-1049-4924-9d13-80344b9614ed","order_by":0,"name":"Wolfram Simon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYFAC5oYPEBpEVICZjAfwa2FsnIHQcgYiRqQWMLuNCC0GxxsbmytqtjHIt3MnPi6cdzhxewPvAfxazhxsbDxz7DaDwWHezcYztx1OnHOALwGvFrMbie0PG9iAWph5t0nzbrudOIOBx4CQlsbGhn+3GeSbQVrmEKulse02A8NhkJYGIrTYg/zS2HebB+wXnmP/jWcwE9Ai2d58sLHh2205+f6zGx/z1KTJzmDvMXyATwsM8CCYzMSoHwWjYBSMglGAFwAAQ3JQSRaGsz4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4324-4481","institution":"Wageningen University and Research (WUR)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Wolfram","middleName":"","lastName":"Simon","suffix":""},{"id":368544820,"identity":"2a138c7c-7fda-4cd2-8a63-a8b04c7f029a","order_by":1,"name":"Hannah Van Zanten","email":"","orcid":"https://orcid.org/0000-0002-5262-5518","institution":"Wageningen University and Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hannah","middleName":"Van","lastName":"Zanten","suffix":""},{"id":368544821,"identity":"ab31b02c-9cb6-48f7-892f-16c207fb53b4","order_by":2,"name":"Renske Hijbeek","email":"","orcid":"https://orcid.org/0000-0001-8214-9121","institution":"Plant Production Systems group, Wageningen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Renske","middleName":"","lastName":"Hijbeek","suffix":""}],"badges":[],"createdAt":"2024-09-17 07:21:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5101296/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5101296/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67229931,"identity":"f9d32771-803a-4a2a-9a7e-02bcf1e58a76","added_by":"auto","created_at":"2024-10-22 16:11:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":263786,"visible":true,"origin":"","legend":"\u003cp\u003eAbsolute amount (A) and relative share (B) of N fertilizers used per scenario in Mt of N and percentage (%) of each fertilizer to total amounts of N applied. Abbreviations: CC: Cover Crops; MMS: Manure management system.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5101296/v1/36b589cda04af309825a5236.png"},{"id":67229930,"identity":"66505d86-371e-4dd2-aff4-42b5bc91d922","added_by":"auto","created_at":"2024-10-22 16:11:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1067512,"visible":true,"origin":"","legend":"\u003cp\u003eTrade-offs and synergies between minimizing land use (Mha), GHGe (kgCO2ew/cap/day) and synthetic N fertilizer (Mt N). Scales of the spider diagrams were normalized and range between 0-1. The grey shaded area is the baseline (BaseFert) impact. LU: Land use; GHG: Greenhouse gas emissions; SYN: Synthetic Nitrogen fertilizer.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5101296/v1/a3eaa66c9efa0e86ddbde2d5.png"},{"id":71061518,"identity":"8b280f3d-7bc2-454f-8ee7-c4c2b4caaf31","added_by":"auto","created_at":"2024-12-10 17:33:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1984529,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5101296/v1/1b6912ad-017a-46d6-a09e-98f6c551f43c.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Diets influence dependency on synthetic nitrogen fertilizers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEurope currently faces a nitrogen (N) crisis. Excessive N losses from agri-food systems have led to environmental challenges such as water pollution and a decline in biodiversity\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Innovative solutions are needed to improve nitrogen use efficiency (NUE) at the food system level, thereby reducing N losses. Especially in Western European countries, where N surplus can reach up to 171 kg/ha\u003csup\u003e2\u003c/sup\u003e. Transitioning towards a circular food system and a more plant-based diet might be a key solution that improves nutrient cycling and reduce reliance on external N inputs\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn a circular food system, biomass losses are prevented or otherwise recovered\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Transitioning towards food system circularity implies searching for practices and technologies that minimize the input of external and finite resources (e.g., synthetic N fertilizer and land), encourage the use of regenerative resources (e.g., organic fertilizers), prevent leakage of valuable resources (e.g., N, phosphorus (P)), and stimulate reuse/recycling of inevitable resource losses (e.g., human excreta) in ways that reduce the environmental impact in the most optimal way\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious research has demonstrated that circularity can effectively reduce greenhouse gas emissions (GHGe) and land use\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, no study has yet assessed the potential of circularity to improve nutrient cycling and reduce external nutrient inputs at a whole food system level using a food system modelling approach. This study aims to assess how redesigning the food system based on circularity improves N cycling. Here, we will focus on N. Nitrogen is often the most limiting nutrient for crop growth, while excessive application has large environmental consequences\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMore precisely, we assess the potential to increase NUE, reduce N surplus, and reduce reliance on synthetic N fertilizer in circular food systems under different dietary scenarios while accounting for potential land use and GHGe trade-offs. Our results show that a circular European food system improves NUE but still depends on synthetic N fertilizer.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eScenarios\u003c/p\u003e \u003cp\u003eTen scenarios were assessed - one baseline and nine circular fertilization strategies - using the Circular Food Systems Model (CiFoS)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. CiFoS is a linear programming optimization model designed to assess pathways to minimize environmental impacts on a food system level. The baseline approximates the current amount of N sourced from manure in the food system\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Furthermore, it matches the current application of synthetic N fertilizer\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, current protein intake\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, current agricultural land use\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and current food waste\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The nine fertilizer scenarios differ in their objective function (minimizing land use, GHGe or synthetic N fertilizer) and dietary scenario (a current, healthy or vegan diet). The current diet (\u0026ldquo;Current\u0026rdquo;) is based on the current FAO protein intake\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The healthy diet (\u0026ldquo;Health\u0026rdquo;) fulfils the EAT-Lancet diet ranges per food group and macro- and micronutrient requirements\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The vegan diet contains no animal-based food and is supplemented with vitamin B12 and omega-3 fatty acids (DHA) (Table\u0026nbsp;1). We assessed NUE, N surplus, synthetic fertilizer N use, GHGe and LU for each scenario.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e. Ten different scenarios per three diets and three objective functions.\u003c/p\u003e \u003cp\u003eNitrogen cycling\u003c/p\u003e \u003cp\u003eNitrogen cycling is assessed by five indicators: synthetic N fertilizer use (per ha and total), NUE, and N surplus (per ha and total). Below, we first show the potential to reduce synthetic N fertilizers on a food system level, after which we show the consequences for NUE and N surplus.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance on nitrogen cycling, land use and greenhouse gas emissions per scenario. Percentage values in brackets (%) show the relative deviation from the baseline scenario \u0026lsquo;BaseFert. \u0026lsquo;Synthetic N Fert(kg/ha)\u0026rsquo; and \u0026lsquo;N surplus(kg/ha)\u0026rsquo; in the scenario \u0026lsquo;BaseFert\u0026rsquo; are calculated for the fertilized area of 134 Mha\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenarios\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSynthetic N Fert.\u003c/p\u003e \u003cp\u003e(kg/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSynthetic N Fert. (Mt)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN Use Efficiency (O/I)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN Surplus (kg/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN Surplus (Mt)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLand Use (Mha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGHGe (kg/cap/yr)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseFert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrentLU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216 (+\u0026thinsp;160%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003cp\u003e(-14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003cp\u003e(+\u0026thinsp;41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e182.4 (+\u0026thinsp;212%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(-20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44\u003c/p\u003e \u003cp\u003e(-74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1206\u003c/p\u003e \u003cp\u003e(-39%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrentGHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (+\u0026thinsp;25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7 (+\u0026thinsp;5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003cp\u003e(-12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.4 (+\u0026thinsp;80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e(+\u0026thinsp;20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e112\u003c/p\u003e \u003cp\u003e(-35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1012\u003c/p\u003e \u003cp\u003e(-49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrentFert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003cp\u003e(-67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003cp\u003e(-67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34 (+\u0026thinsp;100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003cp\u003e(-53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(-60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e139\u003c/p\u003e \u003cp\u003e(-19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003cp\u003e(-9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthLU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166 (+\u0026thinsp;100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003cp\u003e(-39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003cp\u003e(+\u0026thinsp;18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145.8 (+\u0026thinsp;150%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e(-40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41\u003c/p\u003e \u003cp\u003e(-76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1050\u003c/p\u003e \u003cp\u003e(-47%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthGHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003cp\u003e(-7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003cp\u003e(-50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003cp\u003e(+\u0026thinsp;6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.6\u003c/p\u003e \u003cp\u003e(+\u0026thinsp;29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e(-40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e73\u003c/p\u003e \u003cp\u003e(-58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e427\u003c/p\u003e \u003cp\u003e(-78%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthFert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(-95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003cp\u003e(-95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53 (+\u0026thinsp;212%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(-86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(-90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e138\u003c/p\u003e \u003cp\u003e(-20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1678\u003c/p\u003e \u003cp\u003e(-16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVeganLU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282 (+\u0026thinsp;240%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003cp\u003e(-44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003cp\u003e(+\u0026thinsp;59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e220.2 (+\u0026thinsp;277%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(-50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003cp\u003e(-87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e429\u003c/p\u003e \u003cp\u003e(-78%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVeganGHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (+\u0026thinsp;20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003cp\u003e(-61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003cp\u003e(+\u0026thinsp;94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003cp\u003e(+\u0026thinsp;28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(-70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43\u003c/p\u003e \u003cp\u003e(-75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e113\u003c/p\u003e \u003cp\u003e(-94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVeganFert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (+\u0026thinsp;14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.9 \u003c/p\u003e \u003cp\u003e(-65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34 (+\u0026thinsp;100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.9\u003c/p\u003e \u003cp\u003e(+\u0026thinsp;23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(-70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41\u003c/p\u003e \u003cp\u003e(-76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e416\u003c/p\u003e \u003cp\u003e(-79%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePotential to reduce synthetic nitrogen fertilizers on a food system level\u003c/p\u003e \u003cp\u003eThe total amount of N applied is reduced in all the scenarios compared to the BaseFert scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Furthermore, the total amount of synthetic N fertilizer is largely reduced across all scenarios. Currently, 48% of all N applied in Europe comes from synthetic N fertilizers, which equates to 11.1 Mt of N (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). This amount of synthetic fertilizer can be reduced to 3.7 Mt of N (67% reduction) in CurrentFert, 0.5 Mt of N (95% reduction) in HealthFert and 3.9 Mt of N (65% reduction) in VeganFert (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When looking at the amount of synthetic N fertilizer applied per ha, our study shows that when minimizing synthetic fertilizer, the application rates can be changed from the current 83 kg N/ha in the BaseFert scenario to 27 kg N/ha (reduced by 67%), 4 kg N/ha (reduced by 95%) and 95 kg N/ha (increased by 14%) in the CurrentFert, HealthFert and VeganFert scenarios, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The increased application rate in the VeganFert scenario is mainly due to the reduced land use from 172 Mha in the BaseFert scenario to 41 Mha (reduced by 76%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings show considerable potential to reduce synthetic N inputs in the food system. However, synthetic N fertilizers were never entirely removed, and most scenarios included a substantial share of synthetic N fertilizer application, especially in the vegan scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComposition of the fertilizer mix to reduce synthetic nitrogen fertilizer use\u003c/p\u003e \u003cp\u003eThe most substantial relative reductions in synthetic N fertilizer use (from 48% in the BaseFert scenario to 19% and 4% in the CurrentFert and HealthFert scenarios) are mainly achieved by increasing the amount of animal manure in combination with biological N fixation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The relative share of animal manure in the fertilizer mix for crops and grasslands increased from 25% in the BaseFert scenario to 53% and 68% in CurrentFert and HealthFert, respectively. The animals in the HealthFert scenario were increasingly fed with biomass losses (e.g. slaughter waste) and leguminous forage crops (e.g. clover), as well as highly nutritious concentrate crops (e.g. wheat). This strategy increased animal N intake and thus N excretion to maximize N from manure in the food system. Biological N fixation changed from providing 4% of total N applied in the BaseFert scenario to 10% and 12% in the CurrentFert and HealthFert scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In both scenarios, N from manure and biological N fixation increases in relative and absolute terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). Without access to animal manure, the VeganFert scenario reduces synthetic N fertilizer use (compared to BaseFert) by increasing the use of food waste as fertilizer (from 5\u0026ndash;16%), increasing biological N fixation (from 4\u0026ndash;8%), and increasing N from human excreta (from 1\u0026ndash;6% of total N applied). The share of N from crop residues stayed relatively high and constant across all scenarios (providing 11\u0026ndash;16% of all N input to crops and grassland; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). We further found that minimizing synthetic fertilizer does not directly mean minimizing the overall amount of N applied in the food system, as can be seen when comparing the total N applied in HealthGHG to HealthFert (11 vs 15 Mt N; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).HealthFert has a much lower synthetic fertilizer N application than HealthGHG (0.5 vs 5.6 Mt N; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This finding indicates trade-offs between minimizing synthetic N and other environmental impacts on a food system level.\u003c/p\u003e \u003cp\u003eConsequences for nitrogen use efficiency and nitrogen surplus\u003c/p\u003e \u003cp\u003eIn the baseline scenario (BaseFert), the food system\u0026rsquo;s NUE is 0.17, and the average N surplus is 58 kg N/ha, or 10 Mt N losses in total (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In most scenarios, NUE and total N losses are improved, but only in CurrentFert and HealthFert does the N surplus per ha decrease (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The largest NUE and N surplus improvements are achieved when the model\u0026rsquo;s objective function is to reduce synthetic N fertilizer use. When synthetic N fertilizer is minimized with the current diet, NUE is 0.34, and N surplus is 27 kg N/ha, resulting in total losses of 4 Mt N. With healthy diets, NUE would be 0.53 and N surplus 8kg N/ha or 1 Mt N in total. With a vegan diet, NUE would be 0.34 and N surplus 72 kg N/ha or 3Mt N in total. Therefore, the highest NUE and lowest N losses are achieved by changing towards healthier diets with fewer animal-sourced proteins (ASP) (HealthFert scenarios). It is, however, essential to note that even under current consumption patterns, NUE and N surplus can vastly be improved.\u003c/p\u003e \u003cp\u003eTrade-off and synergies with land use and greenhouse gas emissions\u003c/p\u003e \u003cp\u003eWe found that reducing synthetic N fertilizers in the food system can lead to land use and GHGe trade-offs.\u003c/p\u003e \u003cp\u003eLand use\u003c/p\u003e \u003cp\u003eThe current agricultural land use is 172 Mha (BaseFert scenario) (including crop and temporary and permanent grassland). Compared to the BaseFert, all scenarios reduced land use, ranging from 19% (to 139 Mha) in the CurrentFert scenario to 87% (to 22 Mha) in the VeganLU scenario (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The most considerable reductions are achieved when the model\u0026rsquo;s objective function is to reduce land use. For different diets and compared to BaseFert, land use was reduced by 74% for CurrentLU, 76% for HealthLU, and 87% for VeganLU. Vegan diets consistently resulted in the lowest land use, regardless of the model\u0026rsquo;s objective function (land use, GHGe, or minimizing synthetic fertilizer) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared to the \u0026ldquo;Fert\u0026rdquo; scenarios, all nitrogen cycling indicators decreased in their performance when the objective function of the model was to minimize land use. Synthetic N fertilizer increased with 5.8Mt with a current diet, 6.3Mt with a healthy diet and 2.3 Mt with a vegan diet. The highest NUE is achieved with a vegan diet (0.27 for VeganLU). Nitrogen surplus shows more varied results, as total N surplus is also lower with a vegan diet (5 Mt N). However, this is not the case per ha (VeganLU has an N surplus of 220 kg N/ha, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGreenhouse gas emissions\u003c/p\u003e \u003cp\u003eThe EU food system currently emits around 1986 kg CO2eq/cap/year, originating from animal and crop production systems and transportation. Compared to BaseFert, all scenarios have reduced GHGe. Diets greatly impacted the results, ranging from 1800 to 113 kg CO2eq/cap/year. In the GHGe minimization scenarios, emissions could be reduced by 49% (1012 kg CO2eq/cap/year), 78% (427 kg CO2eq/cap/year) and 94% (113 kg CO2eq/cap/year) for the scenarios CurrentGHG, HealthGHG and VeganGHG, respectively, compared to the BaseFert scenario. The lowest emissions were found for the vegan diets, regardless of the objective function (113, 416, 429 kg CO2 eq/cap/year). Furthermore, healthier diets resulted in lower emissions compared to the current diet. When minimizing GHGe, synthetic N fertilizer use increased by 8Mt with a current diet, 5.1Mt with a healthy diet and 0.4 with a vegan diet, when minimizing GHGe compared to minimizing synthetic fertilizer N use. The increased use of synthetic fertilizer indicates that lower emissions can be achieved with a fertilizer mix containing more synthetic N. This is partly explained by the high amounts of manure which cause manure storage emissions, enteric fermentation emissions and manure application emissions, which overall seem to be higher compared to the production and application emissions of synthetic fertilizers.\u003c/p\u003e \u003cp\u003eEnvironmental impact per diet\u003c/p\u003e \u003cp\u003eThe results for current diets show that the minimizing land use scenario has the most synergistic effect on all three impacts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C), as overall impacts for land use, GHGe, and synthetic fertilizers were lowest (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The N fertilizer mix with the highest synergies at current diets (CurrentLU) came from 67% synthetic fertilizer, 13% crop residues, 7% manure, and 7% biological N fixation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). For healthy diets, minimizing land use and GHGe showed equally high synergies between land use, GHGe and synthetic N fertilizers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F). The fertilizer mix of these two scenarios was also very similar in total N applied and share of different fertilizers. In the HealthLU and HealthGHG, respectively 57% and 52% came from synthetic fertilizers, 20% and 18% from animal manure, 12% and 13% from crop residue N, and 4% and 9% from biological N fixation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The only real difference is thus in the lower N fixation in the HealthGHG scenario. This indicates the trade-off between land use and cultivating legumes as indirect N fertilization. The vegan diets were shown to have the lowest overall impacts across land use, GHGe, and amounts of synthetic fertilizer application. The scenario with the lowest overall environmental impact was VeganGHG, which relied mainly on synthetic fertilizer (66% of all N applied), crop residues (13%), food waste (7%), N fixation (7%) and human excreta (6%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003eTo conclude, minimizing land use and GHGe showed synergistic relationships with each other and similar fertilizer mixes. Reducing synthetic fertilizer alone is not a suitable objective, as it can have large trade-offs with land use and GHGe. The total amount of applied fertilizer N seems to be a bigger determining factor for environmental impact than the type of fertilizer used (synthetic or circular/organic fertilizer). These nuanced results demonstrate the importance of assessing fertilization at a food system level, linking production (including fertilization), consumption, and environmental impacts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eNitrogen use efficiency increases in circular food systems\u003c/p\u003e \u003cp\u003eOur findings show that redesigning the food system based on circular principles can positively affect NUE. Most circular scenarios demonstrated an NUE of around 0.3 compared to 0.18 in the baseline. A study focusing on Western Europe also found a positive effect of circularity on NUE, stating that 55% of N in organic recoverable N streams is currently recovered, and that increased nutrient recycling could replace almost half of the external N input\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. A study confirmed our baseline food system NUE of 0.18 precisely and stressed the importance of dietary shifts to reduce N losses on a food system level\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe found that NUE increased most when synthetic N fertilizers were minimized in the model. With healthy diets and minimizing synthetic N fertilizer, NUE can be increased from 0.18 in the baseline to a maximum of 0.53 in the HealthFert scenario. One reason for the highest NUE of 0.53 was the substantial amount of manure in this specific scenario (HealthFert). In this scenario, the model optimized the amount of nitrogen-rich manure produced from animals fed with mainly biomass losses of the food system and legumes. Nitrogen cycling was optimized, but this also led to considerable trade-offs with land use and GHGe. This strategy aligns with a study that found that dairy farms have the most considerable contribution to cycled flows (35% for N) and thus have the potential to increase NUE. The narrative of animals closing nutrient gaps matches the general circularity concept of animals as nutrient recyclers\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Our study finds a similar trend, but with trade-offs on other environmental impacts.\u003c/p\u003e \u003cp\u003eIn contrast, shifting towards vegan diets while reducing GHGe showed synergistic effects across all three impact categories (i.e. land use, GHGe and N cycling). In this case, NUE was increased slightly less (to 0.33) and total synthetic N fertilizer use was reduced (by 65%), but required much less land and GHGe (reduced by 76% and 79% compared to the baseline), only the kg synthetic fertilizer N per ha was increased in this case (by 14%). Several studies align with our findings by stating that more plant-based diets decrease N losses and increase NUE on a food system level\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn most scenarios, NUE and total N losses are improved. However, the N surplus per ha only decreases in CurrentFert and HealthFert, while current N surpluses often already exceed regional thresholds for N impacts in Europe\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Depending on the spatial allocation of agricultural fields across regions, this requires caution when implementing measures to decrease synthetic N fertilizer use. This also shows that strategies that only focus on reducing NUE on a food system level can put pressure on local ecosystems with high N losses per ha, which can negatively affect local biodiversity\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Including such spatially explicit burdens (e.g., biodiversity loss due to N losses into nature areas) in the CiFoS model could be explored in the future.\u003c/p\u003e \u003cp\u003eCircular food systems depend on synthetic N fertilizers\u003c/p\u003e \u003cp\u003eOur results show that all scenarios have some synthetic N fertilizers remaining in the food system. This goes against a widespread conception that food systems would be more environmentally sustainable without synthetic N fertilizer inputs. The most institutionalized example of this standpoint is the EU regulation for organic agriculture that legally prohibits synthetic fertilizers in organic agriculture\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Based on our results, synthetic fertilizers should be reduced but not eliminated. When using integrated soil fertility management strategies, combining synthetic N fertilizer with other organic and circular fertilizers can support sustainable soil management and increase soil organic matter levels\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. As such, synthetic fertilizer could be part of a sustainable European food system strategy by filling the nutrient gaps that cannot be filled by circular fertilizers alone. Without synthetic fertilizers, there would be a real risk of soil mining over time\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The lever for a sustainable food system is not synthetic fertilizers but suboptimal management, which results in the risk of over- or under-fertilization and losses\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFertilization in a plant-based food system\u003c/p\u003e \u003cp\u003eOur results show that the vegan food systems showed the lowest total amount of N applied. When going plant-based, this reduction in N requirements aligns with a study stating that shifting towards more vegan diets can substantially reduce fertilizer use\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Compared to the other dietary scenarios, we found that vegan scenarios depend most on synthetic N fertilizers in relative terms (ranging from 57\u0026ndash;77% of total N applied). So, despite the lower N use in the vegan system, in our study, it is only possible to generate and recycle sufficient N to feed the EU28 population with synthetic fertilizers. Many studies about vegan agriculture and fertilization do not align with these findings, as there is a firm conviction that no or little external inputs are needed\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Apart from synthetic N fertilizers, the central circular fertilization strategy in the vegan food system uses crop residues, N fixation, food waste (composted), and human excreta as fertilizers. In contrast to the literature, where cash and carry (in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e referred to as GreenManureCrop) is often presented as the major fertilization strategy in vegan systems, it was not selected by any scenario in our research.\u003c/p\u003e \u003cp\u003ePractical aspects of using organic fertilizers\u003c/p\u003e \u003cp\u003eEven though we are assessing the potential of increased use of organic fertilizers in this study, we acknowledge that increased use of organic fertilizers presents several practical challenges that require careful management and infrastructure adjustments. To optimize utilization of nutrients from new organic and circular resources, more insights are needed on the composition, the plant-availability of nutrients, and the presence of contaminants\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. One specific issue is managing the fixed nutrient ratios between N and other macro and micro nutrients the plants require. Organic fertilizers are often variable in their nutrient contents and how fast organically bound nutrients become plant-available. The unpredictability of nutrient contents makes synchronizing nutrient availability with crop demand more challenging\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Organic fertilizers are also relatively rich in phosphorus because N has a higher potential for loss. This imbalance between N and other nutrients makes it challenging to provide sufficient amounts of N without overfertilizing other, more available nutrients in a fully circular system\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Additionally, organic fertilizers can contain various pollutants, such as heavy metals and pathogens, which may pose environmental and health risks if not adequately treated and monitored\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.Infrastructure facilitating organic fertilizers, such as human excreta or liquid manure, can pose a further challenge. Safely using human excreta requires reframing \u0026ldquo;waste\u0026rdquo; as a valuable resource and restructuring sanitation and sewage treatment systems to reduce health risks and improve nutrient recovery\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Moreover, transporting organic fertilizers can be challenging due to bulkiness and lower nutrient concentration (compared to synthetic fertilizers)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. These are just a few practical aspects of transitioning towards circular fertilizer that must be carefully considered when redesigning the nutrient cycles of food systems.\u003c/p\u003e \u003cp\u003eTrade-offs\u003c/p\u003e \u003cp\u003eThe trade-offs between minimizing land use, GHGe, and synthetic N fertilizers are complex. Our study indicates that the most minor trade-offs occur between minimizing LU and GHGe. Minimizing synthetic N fertilizer often leads to increased land use and GHGe due to increased N-fixing legumes and intensified need for livestock and manure. This increased demand results from compensating for the reduced N availability from synthetic N fertilizers\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Vegan diets tend to achieve the lowest overall environmental impact, demonstrating synergies with reduced land use, GHGe and synthetic N fertilizer application\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. This is because plant-based diets require less land and result in lower emissions per unit of food produced\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. It is essential to note that reducing synthetic N fertilizers does not necessarily equate to a reduction in the total N applied within the food system, highlighting the need for integrated nutrient management strategies that are assessed with a diverse set of metrics, as was also done in this study. Thus, reducing the food system\u0026rsquo;s environmental impact requires a fertilization mix that balances the synergies and trade-offs between different environmental impacts.\u003c/p\u003e "},{"header":"Online methods","content":"\u003cp\u003eCircular food systems model\u003c/p\u003e \u003cp\u003eThis study is based on the European Circular Food Systems model (CiFoS), first developed by van Zanten et al. (2023)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and later extended by Simon et al., (2024)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. CiFoS is a biophysical food system optimization model written in the General Algebraic Modeling System (GAMS) that incorporates circular principles due to its unique model structure\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The model represents the food system components of food and diets (e.g., nutrition), production (e.g., animal and plant production system, fertilization) and processing (e.g. side-waste streams of organic by-products) and is developed for the EU28 context. The smallest unit of assessment is the agroecological-soil-climate zones for crops. All other components are optimized on a country level using a bottom-up approach\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The modelling work in this study extended the original model with new features regarding fertilization and N cycling. Most parts of the initial model, however, remained unchanged. In the following sections, I will give an overview of the different model components and data inputs used, explain food system N indicators, and end with a scenario description.\u003c/p\u003e\n\u003ch3\u003eHuman nutrition\u003c/h3\u003e\n\u003cp\u003eFor healthy diets, we ensure that CiFoS diets fulfil all nutrient requirements of up to 37 macro and micronutrients, according to the European Food Safety Authority (EFSA)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Nutrients can be switched to design different dietary scenarios (e.g. vegan diets). Vitamin D and iodine requirements were consistently ignored in this study due to mandatory salt fortification for iodine in the EU and implicit limitations in obtaining enough vitamin D from diets alone.\u003c/p\u003e \u003cp\u003eThe nutritional data of the CiFoS food products is based on the FoodData Central Data from the US Department of Agriculture, Agricultural Research Service (USDA)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. We further included limiting food intake per product and family to healthy ranges based on the EAT-Lancet diet\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Apart from the reference scenarios, all scenarios complied with the EFSA nutrient requirements and the EAT-Lancet diet\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.CiFoS includes over 150 food products that can be endogenously produced and processed to model health-optimized and sustainable diets from animal and plant sources. Dietary baseline scenarios are constrained to protein intake levels given by the Food Balance sheet from FAOSTAT\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The many food items and constraint options make CiFoS highly flexible when formulating dietary scenarios.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLand use and land types\u003c/h2\u003e \u003cp\u003eThe total agricultural land (i.e. crop and grassland) in the EU28 is 172 Mha\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. CiFoS distinguishes three land types: arable (i.e. crops and arable grass), permanent managed grassland and unmanaged grassland (i.e. rangelands). Arable land can be cultivated with crops and temporary grassland in crop rotations. Arable land extends were derived from the \u0026rsquo;IIASA-IFPRI cropland map\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, while for grassland extends (managed and unmanaged), the pasture and rangeland datasets from the History Database of the Global Environment (HYDE) dataset was sourced\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The different grassland types were defined as follows: when the pasture dataset overlapped with the cropland, it was defined as temporary grassland. When pasture grassland did not overlap, it was considered permanent grassland (managed). Areas where the rangeland extended and did not overlap with the crop or pasture grassland area were considered rangeland. Land use changes such as deforestation have not been implemented in CiFoS. Therefore, expanding crops or grasslands can never lead to deforestation in our optimization runs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCropping system\u003c/h2\u003e \u003cp\u003eCiFoS represents 43 food crops and eight fodder crops, including three grass types (temporary, permanent and rangeland). Yield and harvested area data come from the Global Spatially-Disaggregated Crop Production Statistics Data for the reference year 2010 (Version 2.0) (SPAM)\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Yield and harvested area for the fodder, tree nuts and vegetable (red, green and other vegetables) crops were taken from the EARTHSTAT dataset \u0026lsquo;Harvested Area and Yield for 175 Crops\u0026rsquo;\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Crop production data sets represent current yield levels. Yields and area data were spatially extracted for agroecological climate-soil zones. These zones were created based on the intersection of the global agroecological zones\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e and the IPCC soil classes derived from the Harmonised World Soil Data Base\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. All crops can be endogenously selected during runtime as area shares of climate-soil-zones.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCrop rotations\u003c/h2\u003e \u003cp\u003eCrop rotation constraints are applied to all annual crops. Crop rotation is modelled as the years a crop needs between two cultivation events to avoid soil-borne diseases. This rotation break number allows the assignment of a specific maximum land share per zone for each crop. We thus convert a temporal crop rotation into spatial area shares, meaning that when a crop needs three years between cropping events, we allow this crop only one-third of the area. The crop rotation data is derived from Pahmeyer (2019)\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCrop nutrient requirements and losses\u003c/h2\u003e \u003cp\u003eCrop fertilization considers the nutrients N and phosphorus. In this paper, however, we only report on N. Fertilizer requirements per crop are defined as the sum of the total amount of above-ground nutrients in the plant and the nutrient losses from the application. This approach ensures a balanced fertilization that dynamically adjusts to yield levels (the reasoning of different N requirement methods is further explained in Simon et al. (2024a)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e). The loss fraction for P was 12.5% of the applied nutrients\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. We calculate the application losses more dynamically for N based on the IPCC equations, which include direct and indirect N2O and run-off/leaching losses\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. These N losses are climate-soil-type dependent and are calculated on a climate-soil zone level using the disaggregated emission factors from IPCC. N-fixation is directly subtracted from the amount of N in the crops. In the model, we allow for 20% overfertilization of nutrients but no underfertilization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFertilizer types\u003c/h2\u003e \u003cp\u003eTo meet the crop nutrient requirements on a climate soil zone level, CiFoS allows for fertilization with the following circular fertilizer types: food waste and losses (composted), animal manure from manure management systems and grazing, human excreta, crop residues, N fixation from cultivated crops, green manure from cover crops and main crops. Synthetic N and mineral P fertilizers are also allowed to fill the gap between the nutrients available from circular fertilizers and those required by crops. Nitrogen deposition is excluded in this study because it depends on the current food production system (especially on livestock systems). As we consider potential future scenarios, we aim to omit these regional N deposition inputs that rely on current production methods. Including these inputs would bias crop production towards areas with currently intense livestock farming and high N deposition (e.g. the Netherlands) due to the availability of \u0026ldquo;free\u0026rdquo; N input.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFood losses and waste\u003c/h2\u003e \u003cp\u003eNutrients from food losses and waste are calculated along all supply chain stages, including post-harvest, processing and packaging, distribution, retail, and consumption losses\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Post-harvest, processing and packaging, and distribution of waste are available as fertilizer in the country of production. To derive the N and P content in the losses, we use the N and P values from the CVB nutrition dataset\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Food losses and waste can be used as fertilizer and feed for farmed monogastric animals and fish to minimize food safety hazards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnimal manure\u003c/h2\u003e \u003cp\u003eAll farmed animals (except farmed fish) in CiFoS produce manure. The amount of nutrients in the manure is a function of nutrients from feed intake and nutrient retention fractions in the animal. There are two manure types in CiFoS. Firstly, when ruminants graze, the excreted manure is considered nutrient input for the grasslands in the grazed soil climate zone. Secondly, when cows are kept indoors, manure enters a manure management system and can be applied anywhere in the country of production.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHuman excreta\u003c/h2\u003e \u003cp\u003eThe CiFoS model has an exogenous and endogenous capability to model nutrients from human excreta. The exogenous method relies on sewage sludge data from EUROSTAT, which states that 36% of sludge produced in EU28 is applied on agricultural land\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. We use this share only in the BaseFert scenario. In the baseline, we assume a dry matter (DM) content of 25% and a nutrient content for sludge of 7.5% and 1.2% of N and P, respectively\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. For this study, we developed another endogenous approach to calculating the nutrient contents in human excreta based on the approach of van Drecht (2003)\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. The nutrient input is derived from protein converted to N by a protein to N fraction of 0.16\u003csup\u003e58\u003c/sup\u003e. N can be converted to P using a N to P fraction of 0.17. Having established the N and P inputs in human food, we can calculate the nutrient outputs using an excretion fraction of 0.37\u003csup\u003e57,59\u003c/sup\u003e. The recovery rates were calculated using the \u0026lsquo;Sanitation technology library\u0026rsquo; that provides recovery ratios for N and P per sanitation technology\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. To calculate nutrients in urine from faeces, the conversion ratios of urine to faeces of (0.16/0.06) and (0.16/0.06) were used for N and P, respectively\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis paper distinguished between the following facility types: sewer systems, septic tanks, improved latrines, unimproved pits, and open defecation\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. The share of the population using each facility type per country was sourced from a household sanitation data set with global coverage\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. With all this information, we could endogenously calculate the N and P coming from human excreta and model them as fertilizer inputs for plant production systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCrop residues\u003c/h2\u003e \u003cp\u003eCrops and grass produce crop residues. This fraction was calculated based on IPCC equations on estimating N in crop residue\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Only the above-ground residues were accounted for, which was everything but main harvested product. The nutrient content and DM of the residues came partly from IPCC and from the USDA nutrient tool\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. The amount of residue was a function of harvested yield\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. In our study, crop residues can only be applied as fertilizer locally in the same climate-soil zone in which the crop was produced.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNitrogen fixation from cultivated crops\u003c/h2\u003e \u003cp\u003eN fixation from cultivated crops is done for all the legume crops and in small amounts also for grass (grass clover mixture assumed). Biological Nfixation is calculated using the following formula following an adjusted approach from\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{r}Nfixation\\left[kg/ha\\right]=Ndfa/100\\text{*}Y\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere Ndfa is the percentage of N uptake derived from biological N fixation, Y is the total above ground yield including residues (expressed in kg N/ha/yr).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGreen manure from cover crops\u003c/h2\u003e \u003cp\u003eCover crops in CiFoS can only be combined after winter sown crops (i.e., barley, other cereals, wheat and rapeseed) in temperate climate zones, on maximum 50% of the arable fields, as cover crops need an early harvest to allow for planting in autumn and a reasonable growing season. Cover crops are assumed to be legumes that fix 23 kg N/ha\u003csup\u003e66\u003c/sup\u003e. We further assume that only 50% of N will be plant-available\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGreen manure from main crops\u003c/h2\u003e \u003cp\u003eUsing the main crop as fertilizer follows a simple approach: All the nutrients in the harvested product are used as fertilizer for crops. Together with the residues, this implies that the whole crop is used as fertilizer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSynthetic N fertilizer and mineral P fertilizer\u003c/h2\u003e \u003cp\u003eSynthetic fertilizers can fill the gap between required and applied nutrients. N and P can be applied independently.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAnimal system\u003c/h2\u003e \u003cp\u003eAnimal production systems encompass dairy, beef, pigs, broilers, and farmed fish (both freshwater and saltwater)\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. We utilized production data for Nile tilapia and Atlantic salmon as representatives for freshwater and saltwater species, respectively. Livestock systems are categorized into three intensity levels, while farmed fish reflect current productivity standards. For each animal type and productivity level, we apply adjusted nutrient requirements. The model also accounts for the nutrient needs of the entire herd structure, including reproductive stock (e.g., heifers in dairy systems) and parent stock (e.g., sows in pig systems). Various feed ingredients can be chosen to meet these requirements, such as co-products, food waste, grass resources, animal by-products, and - if not otherwise utilized in the food system - high-quality biomass like grains. The feed ratio is determined as a model outcome. The animal-source food output from these production systems is influenced by three factors: the quantity and quality of biomass and grass resources available for farm animals, the efficiency of animals in converting biomass into animal-source food, and human nutrient requirements\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCapture fisheries\u003c/h2\u003e \u003cp\u003eCapture fisheries can be selected as an additional source of animal-based food for human consumption and animal feed (limited to by-products). The maximum landings of capture fisheries are obtained from the RAM Legacy Stock Assessment\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e and FAO marine capture data\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. To prevent feed-food competition, we differentiate between the edible yield fractions of all landed fish and their by-products unsuitable for human consumption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eProcessing of food and feed\u003c/h2\u003e \u003cp\u003ePlant and animal-source food products are processed into primary products (e.g., wheat flour) and by-products (e.g., wheat bran). These processing fractions are primarily based on FAO\u0026rsquo;s technical conversion factor document\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. A crop can yield multiple primary products (e.g., white wheat flour, whole wheat flour, wheat grains) and various by-products. Animal by-products represent a portion of the live weight output from each farmed animal system\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. These by-products can be used as animal feed or fertilizer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eTransportation\u003c/h2\u003e \u003cp\u003eFood, feed, and associated by-products can be transported between EU28 countries by lorry. However, food waste, manure, and grassland are restricted to usage within the country of origin. The underlying assumption is that crop and livestock products are processed into food, feed, and by-products within their country of origin before being transported to the destination country for consumption. The distances between countries are calculated using the centroids of each member state and the distances between them.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eGreenhouse gas emissions\u003c/h2\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eGreenhouse gas emissions from animal production system\u003c/h2\u003e \u003cp\u003eWe utilized IPCC tier 2 methodologies to calculate GHGe\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. The GHGe associated with farmed terrestrial animals (dairy, beef, pigs, broilers, and layers) included methane (CH4) and nitrous oxide (N2O) emissions from livestock manure management. Livestock manure management contributes significantly to CH4 and N2O emissions. To estimate methane emissions from manure management, we applied a formula that multiplies volatile solid excretion by the methane conversion factor (specific to each manure management system, or MMS), B0 (the maximum methane production potential of manure), and 0.67 (to convert methane from cubic meters to kilograms of CH4). Volatile solid excretion was calculated by considering the digestibility of protein and organic matter in the feed consumed by each animal species. The CiFoS model internally calculates the amount of animal feed consumed. For N2O, emissions from manure include both direct and indirect emissions, with indirect emissions resulting from the volatilization of ammonia and N2O. N excretion was calculated by subtracting the N retained in meat, milk, or eggs from the total N intake. N2O emissions were then estimated by multiplying N excretion by the appropriate emission factor, which varies according to species and the type of housing system used. In aquaculture systems, N2O emissions are considered within the system. Unconsumed feed and excreta containing N (calculated as the difference between N intake and N retained in body tissues) were multiplied by 1.8% and converted from N to N2O\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Additionally, we accounted for ruminant systems\u0026rsquo; methane (CH4) emissions from enteric fermentation and N2O emissions from grassland fertilization. Methane emissions from enteric fermentation were calculated by multiplying gross energy intake by Ym (the proportion of gross energy in feed converted to CH4) and dividing by 55.65 (the gross energy content of methane). This method adheres to the IPCC tier 2 approach\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. N2O emissions from grassland included both direct and indirect emissions, with indirect emissions arising from the volatilization of ammonia and N2O and nitrate leaching. These emissions are due to N fertilization and manure release during grazing\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eGreenhouse gas emissions from cropping systems\u003c/h2\u003e \u003cp\u003eThe cultivation of crops leads to the release of N2O and CO2 emissions. We utilized the IPCC Tier 1 methodology to estimate crop-related emissions\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. For N fertilization of crops, factors such as fertilizer type, soil characteristics, and climate conditions influence N2O emissions. These emissions can be direct or indirect, with indirect emissions arising from ammonia volatilization and nitrate leaching into the environment. To estimate N2O emissions, we multiplied the applied fertilizer amount by the appropriate emission factor, which varies depending on the specific N fertilizer type and the climate-soil zone. We also included emissions related to the production of synthetic fertilizers using data from the ecoinvent database\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. In our calculations, we considered N2O emissions from drained organic soils, taking into account factors such as land use, climate zone, and soil type (whether peat or non-peat)\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. It is essential to mention that CH4 emissions from rice cultivation were not included, as they were deemed negligible in our analysis. Additionally, CO2 emissions from crop management activities (e.g., tractor fuel use) were not considered.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eGreenhouse gas emissions from compost\u003c/h2\u003e \u003cp\u003eThe composting of food waste results in the release of N2O and CH4 emissions. To estimate N2O emissions, we used the N content in the food waste and applied a N loss fraction of 38%, converting the lost N into N2O. For CH4 emissions, we calculated them based on N losses by converting the N content in the compost into carbon, using the average compost\u0026rsquo;s carbon-to-nitrogen (C:N) ratio, which is ideally around 15. We then converted this carbon content into the total CH4 emissions\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eGreenhouse gas emissions from transportation\u003c/h2\u003e \u003cp\u003eThe transportation of crops, fish, food, by-products, manure, and food waste involves the combustion of fossil fuels, leading to CO2 emissions. We calculated the distances between countries by measuring from one country\u0026rsquo;s centre point to another\u0026rsquo;s centre point. Then, we determined the total ton-kilometres for this transportation. The ton-kilometres were multiplied by an emission factor from the Eco-invent database\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. To estimate the total GHGe, we aggregated them into CO2eq, using a 100-year time horizon (GWP100). A factor of 28 was applied for CH4 and 265 for N2O. The results provided GHG emission totals for the EU28 as a whole and per capita per year\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCircular Principles\u003c/p\u003e \u003cp\u003eCircularity presents a systemic solution by reducing unavoidable waste streams such as food waste and overconsumption of nutrients. If waste is unavoidable, waste streams are reused in the most sustainable manner possible. Additionally, food processing produces by-products such as wheat middlings during flour production. These by-products and waste streams can be used as compost to reduce the need for artificial fertilizers. Furthermore, if waste streams are used as feed for farmed animals, inedible biomass for humans could be transformed into livestock products and manure, leading to increased ecosystem services due to improved soil fertility and less pressure on land\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. The fundamental principles of circular food systems are centred around avoiding and reusing waste and by-product streams to close biomass and nutrient cycles. By defining the healthy diet, we therefore also avoided overconsumption of proteins.\u003c/p\u003e \u003cp\u003eIn this study, circularity was modelled as follows. First, farmed animals can be fed by organic side streams\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Second, the edible ratio of animals is increased to avoid food waste, meaning that humans consume all edible parts of the farmed animals (i.e. offal), and overconsumption is avoided. Third, nutrient recycling is improved by fostering circular fertilization, such as using leguminous crops in crop rotation compost from organic waste streams and crop residues to reduce artificial fertilizer inputs.\u003c/p\u003e \u003cp\u003eScenario description\u003c/p\u003e \u003cp\u003e \u003cem\u003eReference scenario BaseFert\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe reference scenario (BaseFert) aims to represent the current N fertilization mix in the EU28 food system. BaseFert has the objective function to minimize the difference to the current amount of N from animal manure in the food system\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Moreover, BaseFert matches the current application of synthetic N fertilizer on a EU28 level\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and the amount of N used as fertilizer from sewage sludge\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Diets and agricultural land use are fixed to the current protein intake level\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and agricultural land use\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. GHGe match current GHGe on a sector level, meaning for crops, animal and transport components\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. Food can be imported to account for gaps in food supply.\u003c/p\u003e \u003cp\u003eThe nine fertilizer scenarios differ in their dietary scenario (a current, healthy or vegan diet) and objective function (minimizing land use, GHGe or synthetic N fertilizer).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eDiet scenarios\u003c/h2\u003e \u003cp\u003eAll the scenarios had an upper bound for GHGe of 1800 kgCO2/cap/day, close to current emissions. In the optimized scenarios, all circular fertilizer options were allowed so that the model could optimize the fertilizer mix to improve the objective value. These fertilizers differed from the BaseFert scenario: human excreta (endogenous), compost, and green manure (cover crop and main crop).\u003c/p\u003e \u003cp\u003eThe current diet scenario (\u0026ldquo;Current\u0026rdquo;) was constrained to match the current FAO protein intake\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e but was relaxed in the other nutrient requirements. Offals were allowed to be used as food in the Current and the Healthy diets (Health). The Health diet fulfils the EAT-Lancet diet ranges per food group and macro- and micronutrient requirements\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, the Health diet can freely optimize the amount of total protein, protein sources and the share between ASP and PSP. The vegan diet is completely plant-based and can thus not fulfil ASP derived nutrients such as vitamin B12 and omega-3 fatty acids (i.e., DHA). Therefore, we excluded these nutrients from the nutrient requirements (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eObjective functions\u003c/p\u003e \u003cp\u003eEach of the dietary scenarios was assessed with three objective functions: 1) minimizing land use, 2) minimizing GHGe, or 3) minimizing synthetic N fertilizer.\u003c/p\u003e \u003cp\u003eBy using one reference scenario, three different optimization approaches, we generated a total of 10 scenarios.\u003c/p\u003e \u003cp\u003eFood system indicators - nitrogen use efficiency and surplus\u003c/p\u003e \u003cp\u003eNitrogen Use Efficiency (NUE) is defined here on a food system level as NUE\u0026thinsp;=\u0026thinsp;N output / N input. A food system\u0026rsquo;s output is food from plant and animal sources. Inputs, on the other hand, are mainly synthetic N fertilizers and N fixation. Nitrogen was fixed by the main cultivated crop (e.g., soybean) or the leguminous cover crops. These fertilizer sources were the input of nutrients into the food system. All the rest are recycled nutrients. N surplus is defined on a food system level as: N surplus\u0026thinsp;=\u0026thinsp;N input\u0026thinsp;\u0026minus;\u0026thinsp;N output. The input and output are the same as when calculating the NUE.\u003c/p\u003e \u003cp\u003eSoftware and data analysis\u003c/p\u003e \u003cp\u003eAll data transformation, analysis and visualization was performed using R (version 4.2.2)\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. The optimization modelling was performed using the General Algebraic Modeling System (GAMS)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis project received funding from the AVINA Foundation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://avinastiftung.ch/\u003c/span\u003e\u003cspan address=\"https://avinastiftung.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe raw data have been deposited in a GIT repository and are available on request under a license similar to Creative Commons Attribution-NonCommercial-Share A like 4.0 International Public License.\u003c/p\u003e\u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eThe model code has been deposited in a GIT repository and is available on request under a licence similar to Creative Commons Attribution-NonCommercial-Share A like 4.0 International Public License.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRemkes, J. 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(2022) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.6483002\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.6483002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeam, R. C. R: A Language and Environment for Statistical Computing. (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5101296/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5101296/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEurope\u0026rsquo;s nitrogen (N) crisis demands innovative food systems solutions to improve N cycling. This study modelled the potential of different diets and circular fertilization strategies to enhance food system N use efficiency (NUE), reduce N surplus, and minimize reliance on synthetic N fertilizers. Results show that circularity helps to improve NUE and total N losses but does not consistently improve N surplus per ha. Synthetic N fertilizer could be reduced by 95% if healthy diets were consumed in circular food systems, increasing NUE from the current 0.17 to 0.53. The reduction of synthetic N fertilizer led to increased use of manure and showed considerable trade-offs with land use and greenhouse gas emissions (GHGe). In contrast, circular systems in which vegan diets were consumed showed the lowest land use and GHGe and a relatively high NUE (~\u0026thinsp;0.3). This emphasizes the importance of considering trade-offs and synergies between different environmental impacts when redesigning food systems.\u003c/p\u003e","manuscriptTitle":"Diets influence dependency on synthetic nitrogen fertilizers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-22 16:10:58","doi":"10.21203/rs.3.rs-5101296/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55ddd35a-43a3-4e43-a3b6-7b6e1442f4a7","owner":[],"postedDate":"October 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39214903,"name":"Earth and environmental sciences/Environmental sciences"},{"id":39214904,"name":"Biological sciences/Plant sciences"},{"id":39214905,"name":"Physical sciences/Chemistry/Environmental chemistry/Environmental monitoring"},{"id":39214906,"name":"Scientific community and society/Agriculture"},{"id":39214907,"name":"Social science/Environmental studies"}],"tags":[],"updatedAt":"2024-12-10T17:25:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-22 16:10:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5101296","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5101296","identity":"rs-5101296","version":["v1"]},"buildId":"oE6Zbj460LM0Up2FdVbMZ","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}