Enhancing biodiversity with circular food systems

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(Ciska) Veen, Merel Hofmeijer, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5261909/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 Food systems are largely responsible for today’s anthropogenic environmental impacts. Transitioning towards a circular food system is seen as a promising solution to reduce land use (LU) and greenhouse gas emissions (GHGe). But what about biodiversity? The aim of this paper was to assess the potential of enhancing biodiversity in circular European food systems. Two scenarios were assessed with a food systems optimization model: land sharing or sparing while producing healthy food. Our results show that both scenarios can enhance biodiversity while reducing GHGe. The land sparing scenario reduced LU by 81%, depicting great potential for rewilding. However, reduction of LU was achieved via intensification, decreasing agroecosystem’s biodiversity (0 biodiversity score). Conversely, land sharing increased biodiversity in agroecosystems (86% biodiversity score), and LU was maintaned. Both scenarios require to radically redesign today’s food system. Our results demonstrate circular food systems can help enhance biodiversity via land sparing or sharing. Earth and environmental sciences/Ecology/Agroecology Earth and environmental sciences/Ecology/Biodiversity Scientific community and society/Agriculture Agroecosystems circularity food systems Biodiversity land-sparing land-sharing CiFoS model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Biodiversity is rapidly declining globally by about 5% every decade 1 . Food systems are largely responsible for this loss. Directly, biodiversity is impacted by converting natural areas into agricultural land. Indirectly, food systems impact biodiversity loss via their increased contribution to e.g. climate change, pollution and eutrophication 2 , 3 . Besides its environmental impact, current food systems are also responsible for the increased occurrence of non-communicable diseases such as coronary heart disease, diabetes, and cancer due to Western diets 4 . All these impacts are often justified in public debates under the argument that this intensive production configuration of the food system, and its respective diet, is the only way to feed global populations. Therefore, the major challenge for the coming decade is redesigning the food system to respect both human and planetary health 3 , 5 . One of the main underlying reasons for the large environmental impact of food systems in high-income regions, such as Europe, is the current linear extract-produce-consume-discard model 6 . Circularity is, therefore, increasingly seen as a key solution. A circular food system implicitly requires searching for practices and technologies capable of minimizing the need for finite resources (e.g. land, phosphate rock, fossil fuels), encourage the use of renewable ones (e.g. wind and/or solar energy), prevent the loss from the food system of natural resources (e.g. nitrogen (N) and/or phosphorus (P)), and stimulate reuse/recycling of unavoidable losses inherent to the system(e.g. human excreta) in a way that adds the highest value to the food system 7 , 8 , 9 . Recent studies have explored different circularity scenarios and indeed showed that transitioning towards circular food systems reduces the use of finite resources such as mineral fertilization and reduces agricultural land use and GHG emissions while still producing enough healthy food for the European population 10 , 11 . But what about biodiversity? Can we enhance biodiversity in circular food systems without considerable trade-offs with other environmental goals (e.g. GHG emissions)? This question so far remains unanswered. Therefore, this paper aims to assess the potential of enhancing biodiversity in circular European food systems. Our results show that redesigning the European food system based on circularity principles can help enhance biodiversity compared to the current food system. Scenarios to enhance biodiversity We developed two scenarios: one focusing on land sparing and the other on land sharing (Table 1 ). The land sharing and land sparing strategies follow different narratives. Land sparing focuses on sparing areas with the underlying assumption that this spared land would be converted into natural areas. Land sharing, on the other hand, focuses on adopting more biodiversity-friendly practices, e.g., the absence of pesticides or tilling within the agroecosystem (i.e., the land used within the food system) with the aim to enhance biodiversity while simultaneously producing food. To compare the land sharing and land sparing scenarios, we assessed the (agricultural) land use and the associated biodiversity score, linked to agricultural practices. The land use represents all agricultural land (considering the land uses and the farming practices applied) needed to produce enough healthy food to feed the EU27 + UK population assuming a self-sufficient European food system. The biodiversity score was determined by accounting for biodiversity within agricultural areas as follows: each agricultural practice known to enhance biodiversity for a certain taxonomic group obtained one point per ha of used land. The agricultural practices considered were no tillage, organic fertilizer, no pesticide use, cover crops, and natural buffer areas 12 . According to Cozim-Melges et al. 12 , these practices were the most consistent in their positive effects on biodiversity. The biodiversity score is depicted as a percentage of the value obtained when compared to the maximum score possible with all practices (so maximum is 100%). The taxonomic groups considered were arthropods, birds, mammals, bacteria, fungi, earthworms and nematodes. Not all practices enhanced each of the taxonomic groups, and not all practices can be applied in all regions (see methodology in supplementary information for more information). In other words, the practices are specific per taxonomic group and limited by the region where they are applied, e.g., cover crops are limited by the seasonality of chosen crops in a certain agroecological zone in Europe defined in the model. A food systems optimization model called the Circular Food System (CiFoS) model (Van Zanten et al., 2023) was used to compare the Land sparing and Land sharing scenarios with a baseline scenario (Table 1 ). CiFoS is an interactive bio-physical data-driven optimization model that facilitates the selection of food system redesigns by minimizing environmental impacts while producing healthy diets. A healthy diet is assessed as the recommended range of intake levels per food group derived from the EAT-Lancet guidelines plus fulfilling the European Food Safety Authority (EFSA) 13 nutrient requirements. In the land sparing scenario, the objective function of the model was to minimize agricultural land. The objective function for the land sharing scenario was to maximize the biodiversity score. These two scenarios were compared to the baseline production scenario which matches empirical data related to, for example, current crop production systems for domestic use and export with the current areas they are located while minimizing the difference with the current food supply (objective function). Table 1 Scenario descriptions and key scenario settings. Aim/Parameter Baseline Land sparing Land sharing Aim for scenario Represent current crop and livestock situation. Composed of current agricultural land under conventional systems and 6.2% under organic farming Saving of agricultural land for biodiversity Applying alternative practices to enhance biodiversity in agricultural fields Objective function Minimize difference to current food supply Minimize agricultural land use (area spared in ha) Maximize the biodiversity score (% of max score value) Diet Current Healthy diet Healthy diet Results The baseline reaches 2% of the total biodiversity score (considered 100% if all practices were applied in the whole system). Compared to the baseline scenario, the land sparing scenarios reduced agricultural land use by 81% and the biodiversity score became 0%. (Fig. 1 ). The land sharing scenario, on the other hand, resulted in an increased biodiversity score, reaching 86% of the maximum score, and the agricultural area did not change (Fig. 2 ). Both scenarios, land sparing and sharing, produced healthy diets and reduced GHGe. The land sparing scenario reduced GHG emissions by 65% and the land sharing scenario reduced emissions by 44%. What is important to note is that both scenarios resulted in a complete redesign of the European food system. In the next sections we will explain the land sharing and land sparing scenarios and their food system reconfigurations (production and consumption) in more detail. Circular food system redesign in the land sparing scenario In the baseline scenario, 171.7 Mha of agricultural land is used (Fig. 2 , panel A). Of the agricultural land 104.6 Mha is cropland and 67.1 Mha is grassland (permanent grasslands and rangelands). Croplands are reduced to 43.9 Mha (reduced by 58%) and grasslands to 2.3 Mha (reduced by 97%). If we zoom into the different European regions, a clear trend can be seen in which grassland reduction is favoured over reduction of cropland. Nevertheless, in Eastern Europe we see the opposite trend 96% reduction of cropland and 81% reduction in grassland (Fig. 2 , panel B). This shows the importance of accounting for regional differences. To reduce land use the most intensive agricultural practices were used– increasing the yield per ha and therefore decreasing land use. Alternative agricultural practices applied in the baseline, e.g. no pesticide use, were therefore no longer applied. This resulted in a decrease of the biodiversity score in each region, and a shift in practices used (Fig. 3 , panel A) when compared to the overall composition (Fig. 3 , panel B). The reduction in land use in the land sparing scenarios greatly impacted the crop and livestock production systems (Supplementary Figure A 2.1). Regarding crop production systems, the area for most crops was reduced, except for vegetables. Production of vegetables increased, and this can be explained by the underconsumption of vegetables in current European diets. Transitioning towards a healthy diet, therefore, requires an increase in the production of vegetables. If we zoom in on the different regions, we see small changes in crop production systems: southern Europe increase their production of soybean while especially western Europe increased the production of vegetables (supplementary figure A 2.1.2.3). With regard to the animal production systems, a clear reduction in each of the systems is seen. Especially the production of broilers (reduced by 98%), layers (reduced by 95%), and beef (reduced by 87%) largely reduced, while dairy (0%), fish (reduced by 36%) and pig (reduced by 29%) production systems faced no to less reductions (supplementary figure A 4.1.1). Besides changes in production, the land sparing scenario also required dietary changes. Overall protein intake was reduced by 25% from 80g of protein per person per day in the current diet to 60g of protein per day in the spared diet. The share of animal versus plant protein changed from 60% animal-source food to 40% plant source food in the current diet to 20% animal source food and 80% plant source food. Especially the consumption of red meat largely decreased from 6% in the current diet to 2% in the land spared diet (Fig. 4 ). Where in the current diet we see an overconsumption of energy and cholesterol, all nutritional requirements are met in the land spared diet (supplementary figure A 1.2-3). Circular food system redesign in the e land sharing scenario In the land sharing scenario, the area of agricultural land is maintained (Fig. 1 ). In other words, the land sharing scenario offers the possibility to farm less intensive and increase biodiversity within the current agricultural land. To increase the biodiversity score, alternative agricultural practices were applied, which required more land use than land sparing option, but still equal to baseline (Fig. 2 , panel A). Practices such as ‘no pesticide use’ result in lower yields and therefore more agricultural land is needed to produce a healthy diet for the European population compared to the land sparing scenario. The biodiversity score per regions were very similar: 83% for Southern Europe, 90% for Western Europe, 83% for Northern Europe, and 86% for Eastern Europe. Focusing on the practices, not all practices are applied simultaneously everywhere. Natural buffer areas, no tillage and no pesticide use are the most applied alternative practices. Although organic fertilizer application was also widely used, there were still many occasions where artificial fertilizers were applied as well to meet crop nutrient demands. As a result, the maximum biodiversity score for the alternative practice of fertilizer application was not achieved. Similarly, for cover crops, the achieved biodiversity score was lower than the potential maximum because they could only be applied to agroecological zones in temperate regions and under the cultivation of winter crops. The seasonality circumstances limited the use of leguminous cover crops to temperate Northern regions, making cover crops the main constraint, followed by organic fertilizer (Fig. 3 , panel C). The land sharing scenario, just like the land sparing scenario, largely required a redesign of the crop- and livestock production systems. The area for vegetables (8,6 mi ha, + 265%), fruits (7,8 mi ha, + 31%) and forage crops (87.8 mi ha, + 3%) increased compared to the baseline scenario, while the production of cereals (46.9 mi ha, -14%) and oil crops (15.2 mi ha, -7%) decreased (supplementary figure A 2.1.1.2). Furthermore, amount of tubers largely decreased to zero due to the influence of the practice no tillage. However, large differences were observed among regions, e.g., wheat production increased in northern and western Europe while Eastern and Southern Europe increased their production of fruits and vegetables (supplementary figure A 2.2.2.1.). With regards to the animal production systems, the reduction in animal numbers was lower compared to the land sparing scenario. Pigs reduced by 94%, followed by layers (reduced by 71%), broilers (reduced by 64%), beef (reduced by 56%), fish (reduced by 16%) and dairy (reduced by 15%) (supplementary figure A 4.1.1). In the land sharing scenario the intake of protein increased by 4% (83 g per person per day). Despite this increase in protein, the results do show a trend towards more plant-based proteins, with only 25% of the proteins coming from animals and 75% from plants (Fig. 4 ). All nutritional requirements were fulfilled (supplementary figure A 1.2-3). Discussion We have modelled two different pathways for enhancing biodiversity within circular food systems, land sparing and land sharing, and assessed their impact on land use, GHGe emissions and dietary needs. We found that both land sharing and sparing can be applied to circular food systems with distinct but potentially positive effects on biodiversity. Both scenarios also have the potential to synergistically reduce environmental impacts, such as reducing GHG emissions. However, they require different redesigns of the food system, both in terms of production and consumption patterns. Below, we discuss some of the implications of our results for biodiversity and the food systems as well as some of the limitations of our methodology and future research. Redesigning the food system under land sparing and land sharing Both land sharing and sparing scenarios resulted in a reduction of animal source food and an increase in plant source foods, compared to the current food system (baseline scenario), with both scenarios showing an overall reduction in animal numbers. Nevertheless, the protein intake between these scenarios in terms of quantity was different. The overall protein intake was largely reduced (to almost the minimum protein requirement) in the land sparing scenario, while within the land sharing scenario protein intake remained similar to today’s situation. The land sparing scenario’s diet is more in line with results found in previous literature of the use of circular food systems in Europe 10 , 11 , depicting that land sparing will bring more intense dietary changes. The findings of higher protein (and food overall) intake in the land sharing scenario is contrasting to earlier work, which showed that reduced efficiency and lower yields in land sharing systems might increase costs and reduce food accessibility 14 , 15 . Our findings might point out that, while land sharing strategies reduce yield, they also help diversify crops, which might help nutrition in circular food systems, as suggested by Jenkins et al. 16 . The land use changes in both scenarios, but particularly in the land sparing scenario, were variable across the EU27 + UK. In general, our results indicated that agricultural production systems mainly take place in northern and western Europe and near removal of agriculture in southern and eastern Europe, which would affect biodiversity differently across regions as well 18 , 19 , 20 . Additionally, that could shift economic activities and socio-economic dynamics 15 , 21 , 22 , which are important factors for the implementation of such a pathway. While not the scope of this study, it should be an important aspect to consider in future research. The biodiversity score The biodiversity score was 86% out of the 100% in the land sharing scenario and 0% for land sparing. Our results showed that current agricultural land is enough to apply most alternative practices while producing enough healthy food within a self-sufficient European circular food system. The missing 14% of the total 100% can mainly be explained by the low inclusion of the cover crops and the need for artificial fertilizer. In practice, our findings could even underestimate the potential that more diverse cover crops could have in terms of biodiversity and nutrition if implemented in food systems 23 , 24 , with only legumes being available as cover crops in the model. More diverse cropping systems, e.g. living mulches, agroforestry, offer the possibility to cover the soil whole year round to enhance biodiversity 25 and also adds additional nutritional potential 26 . Within the land sharing scenario, it was fertilization that mostly limited the biodiversity score. Applying organic fertilizers are quite well known to have benefits for soils and agroecosystems 27 , 28 , but our results showed that they are limited in availability in line with Simon et al. 11 , making the model require external input of synthetic fertilizers and reducing the score. In other words, although circularity enhance the recycling of biomass, still not enough nutrients are available to fill the nutrient requirements of all crops needed to produce enough healthy food for Europe 11 . Additionally, there are arguments that the implementation of more extensive practices, used in land sharing, would compromise productivity or increase land use 29 , which is contradicted by the findings in this paper. Nevertheless, current literature suggests that combining sparing and sharing is needed to enhance biodiversity holistically 14 , 17 , 30 . Our findings indicate that we could almost reach full potential of an agricultural practices enhancing strategy focussing on species level, from a qualitative instead of a quantitative perspective accounting for, e.g. species abundance or richness. Note, this means biodiversity enhancement of agroecosystems encompassing both above- and below-ground taxonomic groups - a usually ignored group in models (e.g. GLOBIO 31 , GLOBIOM 32 , ReCiPe 2016 33 ). Our study therefore does not indicate the number of species ‘affected’ and if applying alternative practices would safeguard biodiversity in agricultural areas. Instead, our score should be used to indicate how to enhance biodiversity across multiple different taxa with the underlying aim to contribute to reversing biodiversity loss. Methodological limitations and future research To interpret our results, it is important to consider some of the methodological choices made. It is important to note that the biodiversity score developed in this study does not distinguish between types of land use, i.e. we assumed that practices had the same effect in both croplands and grasslands. Grasslands are known to be usually more biodiverse than croplands 19 , 34 , and hence the reduction of grassland in the models could also have effects on biodiversity not considered in this model 35 . Additionally, the biodiversity score could be enhanced by also integrating geographic information accounting for areas with high biodiversity or high proportion of endemism (biodiversity hotspots). The main limitation to such inclusion comes from the absence of data 37 , 38 , 39 , 40 . Future research could benefit and require collaboration and alignment in data collection initiatives on species presence and distribution as well as in identifying species and indicators of particular interest 40 , 41 and their response to agricultural practices. That would help quantify and prioritize areas to be spared and areas to be shared in terms of biodiversity and production combined. Circularity & biodiversity While both scenarios yielded similar food system redesign, it is important to acknowledge that the type of biodiversity enhanced is significantly different and have their own set of challenges. Land use minimization, representing land sparing, is based on returning spared areas to nature. The biodiversity enhanced in this scenario is that of natural areas and not within agroecosystems. Additionally, this enhancement works on the premise that the land spared would be returned to nature and that it would not be affected by spillover effects, however, economic models and literature suggest that this might not happen unless policy/economic measures will come in place 9 , 15 , 17 , 22 , 42 . Applying circularity principles in the land sparing scenario, helps to reduce land use by reducing overconsumption and by recycling biomass within the food system. On the other hand, maximizing the biodiversity score, representing land sharing, would directly increase the biodiversity within agroecosystems. Applying circularity principles helps to increase the share of organic fertiliser, replacing artificial fertilisers. Furthermore, decreasing land use via e.g. recycling strategies, allows for compensating the increase in land use due to the yield reduction caused by some of the practices (e.g. no pesticide use). Without circularity, not enough land might have been available within the land sharing scenario to produce enough healthy food to the European population. Whether this will lead to enhancing biodiversity in the surrounding natural areas is not assured (e.g. due to reduced eutrophication and pesticide use) and would require more research, given existing literature on the need for a minimum size of natural areas 43 . Current literature has already shown that in terms of global biodiversity conservation, both strategies on their own would be sufficient to prevent biodiversity loss 30 . Our study shows the feasibility and the different configurations emerging from these two different strategies. We contribute to current knowledge by showing the contrast of these two different scenarios and their points of convergence in circular food systems. Future research could integrate these findings to more complex biodiversity interactions between the natural and agroecosystem biodiversity to model what kind of configuration would a circular food system have when using both strategies in a complimentary manner as suggested by Alkemade et al. 43 . Social and economic implications of these changes would also need to be accounted for, as suggest by other studies 21 , 22 . Our results help to shed light on the different aspects policies need to engage with in regards to production and consumption for each specific scenario. Land sparing would likely require policies to insure the spared land will be returned to nature, and on the other hand, land sparing might need policies to quick start the adoption of alternative practices. Overall, our results showed that applying circularity principles will help to enhance biodiversity, either via land sharing or sparing. Moreover, despite differences in land use and diet between scenarios there were also points of convergence, such as the reduction of animal-sourced foods and increase in vegetables, depicting these scenarios are not irreconcilable. Methods Modelling circular food systems To assess the potential of circularity for enhancing biodiversity we built on the CiFoS model established in van Zanten et al. 23 . The CiFoS model is a biophysical data-driven food system optimization model coded in General Algebraic Modelling System (GAMS), spanning a wide range of aspects, starting from the land and mineral resources available in a system, going to the flow of matter and energy, e.g. nutrients that encompass the dynamics of the system, such as nitrogen addition through fertilizers or recycling of waste, to human nutritional requirements. Its generic make-up makes it applicable to different regions and aggregation of data to larger regional scales, i.e. subcontinental Europe. Currently the model is already an established tool for assessing the potential of circularity in Europe. In the current paper, we model the region of Europe, EU27 + UK, with a total area of agricultural land available in the model of 171.7 Mha (FAOSTAT). The model uses data from SPAM 44 to define the yields and crops available in the food system and FAOSTAT data 46 , 47 for the available land and includes 43 food crops and eight fodder crops, including three different grass types (temporary, permanent and rangeland). To account for biodiversity, Europe is divided into 4 regions, the 4 sub-continental divisions of western, eastern, northern and southern Europe (see supplementary figures A), and each region is further divided into agroecological zones within them (GAEZ version 4, 33 classes). Each sub-continent/agroecological zone has land classes which are divided in croplands (temporary grasslands included), permanent pastures, and rangelands. Finally, the land classes are also divided by their soil zones, which were grouped from the original definition of the International Panel for Climate Change (IPCC) used in the model, to simpler “organic” and “mineral” soil groups. The model operates within the boundary to provide the current human population with a healthy diet, determined by caloric, macro and micro nutrient intake. For nutrient requirements, the CiFoS model accounts for the 42 most important macro- and micronutrients; and those are met combining a range of food groups (i.e. grains, tubers, vegetables, fruit, dairy, red meat, chicken, eggs, fish, legumes, nuts/seeds, oil fat, sugar and other). For the scenarios in our model, we assume a diet limited by maximum and minimum intakes as proposed by the EAT-Lancet reference range. These values are considered as constraints to be respected in the model. Greenhouse gas emissions accounted for are transportation, crops, fertilizer and livestock using the IPCC tier 1 methodology. We follow the same protocol as in Van Zanten et al. 10 to create a baseline (“Agribase”) of our current food systems in the model and use that as the baseline comparison for the other scenarios where we maximize the biodiversity score (land sharing) and minimize the land use (land sparing). The baseline scenario runs on the objective function of reducing the protein supply difference from the model to the values found in the statistical data from the FAO 46 , 47 , averaged between 2010 and 2019 10 , while respecting constraints on the land use and share of land use of crop families, and protein supply type, to determine the food system characteristics. For the full dataset see extended data. Including biodiversity-enhancing practices to the CiFoS model To account for biodiversity enhancement within agricultural areas we have added five alternative agricultural practices that can be chosen by the CiFoS model and are known to enhance biodiversity across a range of taxonomic groups 12 in agroecosystems. The practices added to the model are “no tillage”, “organic fertilizer”, “no pesticide use”, “cover crops”, and “natural buffer areas” (Fig. 3 ). These practices were chosen based on their known effects on multiple taxa in the agroecosystem, allowing us to use these same taxonomic groups as target for biodiversity in this model 12 . For each of these practices we not only assessed how they affect biodiversity (see below “Scoring biodiversity the CiFoS model”), but also included how their application would impact other variables of the food system, e.g. yield (see “Effects of alternative practices on the food systems”), as those are key parameters in CiFoS for designing the food systems (Table 1 ). Effect of practices on biodiversity This study considers biodiversity in agroecosystems from the species level and focus on taxonomic groups with known relevance for agroecosystems and studied to the five agricultural practices included in the model, as established in the literature 12 . The taxonomic groups considered for the biodiversity assessment in the model comprehend both aboveground biodiversity, represented by the taxa arthropods, birds and mammals, and belowground biodiversity, represented by bacteria, fungi, earthworms and nematodes. We considered the impacts of practices on biodiversity as identified in Cozim-Melges et al. 12 as summarized in Table 1 . Practices identified in the literature as positive for a taxa would be assigned a “+” sign, while practices with no effect observed would be assigned “NOE”. While we had mostly complete data for how all taxa respond to each practice, some gaps existed. Practices not found to be studied for a combination practice-taxa had their “NOE” highlighted gray. We have opted to adopt a conservative approach to biodiversity enhancement with practices when data was not found. Therefore, practices with NOE and practices with no data available for that taxa were both considered neutral for biodiversity enhancement of that taxon and for that given practice. We have conducted analysis and ran the model replacing the non-available data by positive effects and by expert opinion to check for the sensitivity of the model. The changes did not affect the food system configuration or practices. Effects of practices on the food system – yield and fertilization The five practices integrated into the model structure affected two main aspects of the model: yield (natural buffer area, no tillage, no pesticide) and fertilization type and source (organic fertilizer, cover crop). For the values used for effect of the practices on the model, we have used literature data and expert knowledge from both the Farming Systems Ecology and Crop System Analysis groups in Wageningen University, coherent with the European context. We have opted to use the most conservative values when no data was available, i.e. considering absence of data as no effect, for how these alternative practices impact yield or fertilization, in order to avoid overshooting our results. For the specific case of no pesticide use limited data were available, and hence we have extrapolated the values modelled for a reduction of 50%, as per the EU 30 agenda’s ambitions, to simulate our coefficients of loss for the complete absence of pesticides. The R script and the dataset can be found in the GIT. A full description of the equations and values used based on the literature can be found in Fig. 6 and table, values for each coefficient used can be found in supplementary material C. These effects were added directly to the dataset as additional options through the use of R script 47 . The GAMS model would then consider all these options in the dataset, with both different biodiversity scores available for the combination of practices and different yields and sources of fertilization, and choose the optimal solution to maximize biodiversity while respecting the constraints of a healthy diet and maximum land available. Table 2. Definition of practices, their effects and data sources used Scoring biodiversity in the CiFoS model To assess biodiversity enhancement we developed a biodiversity score which is defined as the potential impact the adoption of certain agricultural practice(s) to biodiversity of the food system. This scoring system is based on the impact the alternative practices mentioned above have on the biodiversity of the target taxonomic groups in agroecosystems. Using the matrix in Fig. 4 , we have translated all positive signs as a positive point of a practice to that given taxa, while all “NOE” as no points. Hence the potential of a practice to enhancing biodiversity is given as the sum of all taxa positively affected by that practice, as seen in equation (I), where p refers to the practice. $$\:Score\left(p\right)=\:{\sum\:}_{practice}Score\left(taxa\right)$$ Some practices were studied for more taxa than others, while some taxa were positively affected by more practices. Thus, evaluating biodiversity by comparing the individual taxa affected would not be a correct comparison, given their different maximum and minimum scores. We have the extended values in the model for the contribution to the score of each hectare of each practice and taxa (supplementary material B), but have maximized and reported a unified score across taxa for a practice. The calculation of the biodiversity score can be seen in equation (II), where, BioS is the biodiversity score(percentage), c and aez are both the sub-continent and the agroecological zone (measured in ha), and p is the practice. We simplified potential interactions and considered a linear interaction, i.e. the more positive practices enhancing taxa are applied, the higher the likelihood that biodiversity will be enhanced. Given all effects are not measured as magnitude, a higher score does not represent magnitude of effect, but rather the number of practices with positive effects across all taxa being used in the model. $$\:BioS\left(c,\:aez\right)=\:\frac{\frac{{\sum\:}_{c,\:aez}Score\left(p\right)*area\left(p\right)}{Tot\:Area\:\left(c,aez\right)}}{Max\:Biodiv\:Score\:}$$ The biodiversity score is given then per hectare and is the total sum of the scores of the practices applied in a an agroecological zone inside each sub-continent divided by the total land use of that area, and represents the potential to enhance biodiversity across these seven taxa per hectare. Hence, our biodiversity score is a metric of the potential for biodiversity enhancement in comparison with current systems. The final biodiversity score given by the model is given per sub-continent and a weighted average of the biodiversity score for Europe is also calculated. Declarations Acknowledgements. This project received funding from the AVINA foundation (www.circularfoodsystems.org). We would like to thank Laura Gerwien and Thomas Maindl with regards to model advice. Ethics declaration. The authors declare no competing interest. Author contributions. FM (Felipe Cozim Melges) and HvZ (Hannah H.E. van Zanten, devised the idea for the study. All authors helped strengthen the methodology of the paper and reviewed the results. FM and HvZ wrote the main manuscript text. All authors contributed to, reviewed and approved the final manuscript submitted. Data availability. All additional model data is available in the supplementary materials. Model information can be requested directly with the corresponding author. Code availability. Custom R scripts developed for the analyses and visualizations in this manuscript and the raw data have been deposited in the GIT repository and are available on request under a licence. References Campbell, B. M., Beare, D. J., Bennett, E. 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Supplementary Files SupplementaryFiguresA.html Supplementary Figures A SupplementarymaterialBBiodivscore.xlsx Supplementary material B SupplementarymaterialCCoefficientsofpractices.xlsx Supplementary material C - Coefficients of practices Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board 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-5261909","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":386676605,"identity":"7e76225a-758e-4272-8d96-d1f329426bba","order_by":0,"name":"Felipe Cozim Melges","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIie3RsWqEMBjA8YigS6zrBzfcK3hTO8j5KorQySsHXW64IQ7NVHBV6FN0uKlDJGAXoavgVApO3QqFLkc/m+FaaDIXmj9EE8kPgxJis/3BfOYyoaYOw8sFDlc9ONMQKpwfBHB46dfaM5Dv65nQyEz8spTbHVmGoby5H3awrsL+fRE8kCstoS2TdU9WDWv5WPSQN/XmsKATudaRBDImA07SqC35uOGQR0NwGKkgGde9ZfmM5IhEOkiOkCdP/WQm4CBhSLqZMFhHpPDMhOLB6g5WzS2SooMUhsvzjzsBeuJL+bbdx/jFHqex2MdJWMmX6FXEWaUhc676HaqMqTv8vvdETiXGrTabzfYv+wQe81u6T6vQGAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0833-8973","institution":"Wageningen University and Research","correspondingAuthor":true,"prefix":"","firstName":"Felipe","middleName":"Cozim","lastName":"Melges","suffix":""},{"id":386676606,"identity":"f8a2589e-8971-4704-961d-e7794d2f568d","order_by":1,"name":"Raimon Ripoll-Bosch","email":"","orcid":"https://orcid.org/0000-0002-1234-7015","institution":"Wageningen University and Research","correspondingAuthor":false,"prefix":"","firstName":"Raimon","middleName":"","lastName":"Ripoll-Bosch","suffix":""},{"id":386676607,"identity":"f0b1cfd7-5f8f-476f-82a7-4a78dbbacc35","order_by":2,"name":"G.F. (Ciska) Veen","email":"","orcid":"","institution":"Netherlands Institute of Ecology","correspondingAuthor":false,"prefix":"","firstName":"G.F.","middleName":"(Ciska)","lastName":"Veen","suffix":""},{"id":386676608,"identity":"652542c2-1810-475c-bf03-70431bf6349e","order_by":3,"name":"Merel Hofmeijer","email":"","orcid":"","institution":"Wageningen University and Research","correspondingAuthor":false,"prefix":"","firstName":"Merel","middleName":"","lastName":"Hofmeijer","suffix":""},{"id":386676609,"identity":"981084bb-a312-4694-a6cf-73b43884d3a6","order_by":4,"name":"Wolfram Simon","email":"","orcid":"https://orcid.org/0000-0002-4324-4481","institution":"Wageningen University and Research (WUR)","correspondingAuthor":false,"prefix":"","firstName":"Wolfram","middleName":"","lastName":"Simon","suffix":""},{"id":386676610,"identity":"8fedbc61-d743-46e4-ad74-b93996f3e282","order_by":5,"name":"Dirk van Apeldoorn","email":"","orcid":"","institution":"Wageningen University \u0026 Research","correspondingAuthor":false,"prefix":"","firstName":"Dirk","middleName":"van","lastName":"Apeldoorn","suffix":""},{"id":386676611,"identity":"d5896315-3004-412d-a1e5-39131c313859","order_by":6,"name":"Hannah Van Zanten","email":"","orcid":"https://orcid.org/0000-0002-5262-5518","institution":"Wageningen University and Research","correspondingAuthor":false,"prefix":"","firstName":"Hannah","middleName":"Van","lastName":"Zanten","suffix":""}],"badges":[],"createdAt":"2024-10-14 14:32:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5261909/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5261909/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70924668,"identity":"09c3ed9e-04f0-40cd-a96b-d5a2e98fa0dd","added_by":"auto","created_at":"2024-12-09 09:05:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90641,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of results. Results regarding the land sparing and land sparing scenarios, compared to the current baseline European food systems scenario in terms of land used, land spared, biodiversity score and GHGe emissions. Results highlighted green indicate a positive change compared to the baseline. GHGe refers to greenhouse gas emissions, Mt refers to million(s) tons, and Mha refers to million(s) hectares.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5261909/v1/685d9d2dd5b4e3bf1a9f8d98.png"},{"id":70924664,"identity":"dc5fb68a-89c9-4d78-aa56-3f3bf3af026c","added_by":"auto","created_at":"2024-12-09 09:05:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67696,"visible":true,"origin":"","legend":"\u003cp\u003eLand use in the land sparing and land sharing scenario compared to current land use (Baseline). On the left (panel A), the total value for the EU27+UK, and on the right (panel B), the different values for each sub region. CL refers to crop-land and GL refers to grass-land.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5261909/v1/9054666bb394ca4f707d20cd.png"},{"id":70924656,"identity":"5134b317-5e57-41ea-8b24-033a6865c5c7","added_by":"auto","created_at":"2024-12-09 09:05:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76623,"visible":true,"origin":"","legend":"\u003cp\u003eDepiction of the share of practices and their combination that contributed to the biodiversity enhancement score. The percentages are regarding their share in the final score. NBF refers to the practice natural buffer areas. Panel A illustrates the shares in each region and panel B the overall contribution of practices in Europe. The bar chart (panel C) indicates the proportion of individual practices applied in the system across combinations – zero meaning it was never applied and one meaning it was applied in all areas used.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5261909/v1/f6322f2a6992e491d0c53f83.png"},{"id":70926664,"identity":"3cec0bd4-831f-4493-99c3-f306d57972fe","added_by":"auto","created_at":"2024-12-09 09:13:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":155226,"visible":true,"origin":"","legend":"\u003cp\u003eDaily per capita protein consumption per food group and scenarios. The triangles indicate the minimum (triangle pointing up) protein consumption recommendations. The pattern differentiates between animal and plant sourced proteins.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5261909/v1/b06f984b26ed9a1a2ebd8ddd.png"},{"id":70924677,"identity":"12c7a19e-13d5-4adc-8b83-486c942a12de","added_by":"auto","created_at":"2024-12-09 09:05:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":41734,"visible":true,"origin":"","legend":"\u003cp\u003eBiodiversity response of the taxa to the five practices included in the model according to Cozim-Melges et al., 2024. Positive signs indicate that the practice was found to be mostly positive to the taxa. NOE refers to No observed effects, symbolizing no change. Gray coloring indicates that NOE was conservatively considered but no data was found for the effect of that practices in Cozim-Melges et al., 2024.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5261909/v1/e93bfe13e74c31eba1aa383a.png"},{"id":70926668,"identity":"f2b4a158-6f78-4c59-94f9-fe2b24b3ca72","added_by":"auto","created_at":"2024-12-09 09:13:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":118869,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of where the added practices are in the model and their respective effects on variables in the model. Further descriptions are given in table 2.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5261909/v1/bee8418d2e47c2e32122e56b.png"},{"id":74681087,"identity":"ef777d3a-97da-4b51-8fa9-2464153321f0","added_by":"auto","created_at":"2025-01-24 15:53:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1428656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5261909/v1/d89837fe-8824-44cd-890a-4ff656dfaafd.pdf"},{"id":70926669,"identity":"04f2e6a1-6c7b-4ba1-97bc-80e27cc7d718","added_by":"auto","created_at":"2024-12-09 09:13:44","extension":"html","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2163453,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figures A\u003c/p\u003e","description":"","filename":"SupplementaryFiguresA.html","url":"https://assets-eu.researchsquare.com/files/rs-5261909/v1/322f423c7e8674a87a0d2d59.html"},{"id":70924675,"identity":"5de9b2b4-ad22-47ef-a7ef-6b99197ca696","added_by":"auto","created_at":"2024-12-09 09:05:43","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":46092,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary material B\u003c/p\u003e","description":"","filename":"SupplementarymaterialBBiodivscore.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5261909/v1/605e519cfb2a2d00275929ea.xlsx"},{"id":70924663,"identity":"aaef67a9-84cd-486e-bfe6-b80313812bfa","added_by":"auto","created_at":"2024-12-09 09:05:43","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17380,"visible":true,"origin":"","legend":"Supplementary material C - Coefficients of practices","description":"","filename":"SupplementarymaterialCCoefficientsofpractices.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5261909/v1/a341622d454a3aead9d3e83d.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Enhancing biodiversity with circular food systems","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBiodiversity is rapidly declining globally by about 5% every decade\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Food systems are largely responsible for this loss. Directly, biodiversity is impacted by converting natural areas into agricultural land. Indirectly, food systems impact biodiversity loss via their increased contribution to e.g. climate change, pollution and eutrophication\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Besides its environmental impact, current food systems are also responsible for the increased occurrence of non-communicable diseases such as coronary heart disease, diabetes, and cancer due to Western diets\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. All these impacts are often justified in public debates under the argument that this intensive production configuration of the food system, and its respective diet, is the only way to feed global populations. Therefore, the major challenge for the coming decade is redesigning the food system to respect both human and planetary health\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne of the main underlying reasons for the large environmental impact of food systems in high-income regions, such as Europe, is the current linear extract-produce-consume-discard model\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Circularity is, therefore, increasingly seen as a key solution. A circular food system implicitly requires searching for practices and technologies capable of minimizing the need for finite resources (e.g. land, phosphate rock, fossil fuels), encourage the use of renewable ones (e.g. wind and/or solar energy), prevent the loss from the food system of natural resources (e.g. nitrogen (N) and/or phosphorus (P)), and stimulate reuse/recycling of unavoidable losses inherent to the system(e.g. human excreta) in a way that adds the highest value to the food system\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Recent studies have explored different circularity scenarios and indeed showed that transitioning towards circular food systems reduces the use of finite resources such as mineral fertilization and reduces agricultural land use and GHG emissions while still producing enough healthy food for the European population\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBut what about biodiversity? Can we enhance biodiversity in circular food systems without considerable trade-offs with other environmental goals (e.g. GHG emissions)? This question so far remains unanswered. Therefore, this paper aims to assess the potential of enhancing biodiversity in circular European food systems. Our results show that redesigning the European food system based on circularity principles can help enhance biodiversity compared to the current food system.\u003c/p\u003e\n\u003ch3\u003eScenarios to enhance biodiversity\u003c/h3\u003e\n\u003cp\u003eWe developed two scenarios: one focusing on land sparing and the other on land sharing (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The land sharing and land sparing strategies follow different narratives. Land sparing focuses on sparing areas with the underlying assumption that this spared land would be converted into natural areas. Land sharing, on the other hand, focuses on adopting more biodiversity-friendly practices, e.g., the absence of pesticides or tilling within the agroecosystem (i.e., the land used within the food system) with the aim to enhance biodiversity while simultaneously producing food. To compare the land sharing and land sparing scenarios, we assessed the (agricultural) land use and the associated biodiversity score, linked to agricultural practices. The land use represents all agricultural land (considering the land uses and the farming practices applied) needed to produce enough healthy food to feed the EU27\u0026thinsp;+\u0026thinsp;UK population assuming a self-sufficient European food system. The biodiversity score was determined by accounting for biodiversity within agricultural areas as follows: each agricultural practice known to enhance biodiversity for a certain taxonomic group obtained one point per ha of used land. The agricultural practices considered were no tillage, organic fertilizer, no pesticide use, cover crops, and natural buffer areas\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. According to Cozim-Melges et al.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, these practices were the most consistent in their positive effects on biodiversity. The biodiversity score is depicted as a percentage of the value obtained when compared to the maximum score possible with all practices (so maximum is 100%). The taxonomic groups considered were arthropods, birds, mammals, bacteria, fungi, earthworms and nematodes. Not all practices enhanced each of the taxonomic groups, and not all practices can be applied in all regions (see methodology in supplementary information for more information). In other words, the practices are specific per taxonomic group and limited by the region where they are applied, e.g., cover crops are limited by the seasonality of chosen crops in a certain agroecological zone in Europe defined in the model.\u003c/p\u003e \u003cp\u003eA food systems optimization model called the Circular Food System (CiFoS) model (Van Zanten et al., 2023) was used to compare the Land sparing and Land sharing scenarios with a baseline scenario (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). CiFoS is an interactive bio-physical data-driven optimization model that facilitates the selection of food system redesigns by minimizing environmental impacts while producing healthy diets. A healthy diet is assessed as the recommended range of intake levels per food group derived from the EAT-Lancet guidelines plus fulfilling the European Food Safety Authority (EFSA)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003enutrient requirements. In the land sparing scenario, the objective function of the model was to minimize agricultural land. The objective function for the land sharing scenario was to maximize the biodiversity score. These two scenarios were compared to the baseline production scenario which matches empirical data related to, for example, current crop production systems for domestic use and export with the current areas they are located while minimizing the difference with the current food supply (objective function).\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScenario descriptions and key scenario settings.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAim/Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLand sparing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLand sharing\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAim for scenario\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRepresent current crop and livestock situation. Composed of current agricultural land under conventional systems and 6.2% under organic farming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaving of agricultural land for biodiversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApplying alternative practices to enhance biodiversity in agricultural fields\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObjective function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimize difference to current food supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimize agricultural land use (area spared in ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximize the biodiversity score (% of max score value)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHealthy diet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe baseline reaches 2% of the total biodiversity score (considered 100% if all practices were applied in the whole system). Compared to the baseline scenario, the land sparing scenarios reduced agricultural land use by 81% and the biodiversity score became 0%. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The land sharing scenario, on the other hand, resulted in an increased biodiversity score, reaching 86% of the maximum score, and the agricultural area did not change (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both scenarios, land sparing and sharing, produced healthy diets and reduced GHGe. The land sparing scenario reduced GHG emissions by 65% and the land sharing scenario reduced emissions by 44%. What is important to note is that both scenarios resulted in a complete redesign of the European food system. In the next sections we will explain the land sharing and land sparing scenarios and their food system reconfigurations (production and consumption) in more detail.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCircular food system redesign in the\u003c/b\u003e \u003cb\u003eland sparing\u003c/b\u003e \u003cb\u003escenario\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the baseline scenario, 171.7 Mha of agricultural land is used (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, panel A). Of the agricultural land 104.6 Mha is cropland and 67.1 Mha is grassland (permanent grasslands and rangelands). Croplands are reduced to 43.9 Mha (reduced by 58%) and grasslands to 2.3 Mha (reduced by 97%). If we zoom into the different European regions, a clear trend can be seen in which grassland reduction is favoured over reduction of cropland. Nevertheless, in Eastern Europe we see the opposite trend 96% reduction of cropland and 81% reduction in grassland (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, panel B). This shows the importance of accounting for regional differences. To reduce land use the most intensive agricultural practices were used\u0026ndash; increasing the yield per ha and therefore decreasing land use. Alternative agricultural practices applied in the baseline, e.g. no pesticide use, were therefore no longer applied. This resulted in a decrease of the biodiversity score in each region, and a shift in practices used (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, panel A) when compared to the overall composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, panel B).\u003c/p\u003e \u003cp\u003eThe reduction in land use in the land sparing scenarios greatly impacted the crop and livestock production systems (Supplementary Figure A 2.1). Regarding crop production systems, the area for most crops was reduced, except for vegetables. Production of vegetables increased, and this can be explained by the underconsumption of vegetables in current European diets. Transitioning towards a healthy diet, therefore, requires an increase in the production of vegetables. If we zoom in on the different regions, we see small changes in crop production systems: southern Europe increase their production of soybean while especially western Europe increased the production of vegetables (supplementary figure A 2.1.2.3). With regard to the animal production systems, a clear reduction in each of the systems is seen. Especially the production of broilers (reduced by 98%), layers (reduced by 95%), and beef (reduced by 87%) largely reduced, while dairy (0%), fish (reduced by 36%) and pig (reduced by 29%) production systems faced no to less reductions (supplementary figure A 4.1.1).\u003c/p\u003e \u003cp\u003eBesides changes in production, the land sparing scenario also required dietary changes. Overall protein intake was reduced by 25% from 80g of protein per person per day in the current diet to 60g of protein per day in the spared diet. The share of animal versus plant protein changed from 60% animal-source food to 40% plant source food in the current diet to 20% animal source food and 80% plant source food. Especially the consumption of red meat largely decreased from 6% in the current diet to 2% in the land spared diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Where in the current diet we see an overconsumption of energy and cholesterol, all nutritional requirements are met in the land spared diet (supplementary figure A 1.2-3).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCircular food system redesign in the e\u003c/b\u003e \u003cb\u003eland sharing\u003c/b\u003e \u003cb\u003escenario\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the land sharing scenario, the area of agricultural land is maintained (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In other words, the land sharing scenario offers the possibility to farm less intensive and increase biodiversity within the current agricultural land. To increase the biodiversity score, alternative agricultural practices were applied, which required more land use than land sparing option, but still equal to baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, panel A). Practices such as \u0026lsquo;no pesticide use\u0026rsquo; result in lower yields and therefore more agricultural land is needed to produce a healthy diet for the European population compared to the land sparing scenario. The biodiversity score per regions were very similar: 83% for Southern Europe, 90% for Western Europe, 83% for Northern Europe, and 86% for Eastern Europe. Focusing on the practices, not all practices are applied simultaneously everywhere. Natural buffer areas, no tillage and no pesticide use are the most applied alternative practices. Although organic fertilizer application was also widely used, there were still many occasions where artificial fertilizers were applied as well to meet crop nutrient demands. As a result, the maximum biodiversity score for the alternative practice of fertilizer application was not achieved. Similarly, for cover crops, the achieved biodiversity score was lower than the potential maximum because they could only be applied to agroecological zones in temperate regions and under the cultivation of winter crops. The seasonality circumstances limited the use of leguminous cover crops to temperate Northern regions, making cover crops the main constraint, followed by organic fertilizer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, panel C).\u003c/p\u003e \u003cp\u003eThe land sharing scenario, just like the land sparing scenario, largely required a redesign of the crop- and livestock production systems. The area for vegetables (8,6 mi ha, +\u0026thinsp;265%), fruits (7,8 mi ha, +\u0026thinsp;31%) and forage crops (87.8 mi ha, +\u0026thinsp;3%) increased compared to the baseline scenario, while the production of cereals (46.9 mi ha, -14%) and oil crops (15.2 mi ha, -7%) decreased (supplementary figure A 2.1.1.2). Furthermore, amount of tubers largely decreased to zero due to the influence of the practice no tillage. However, large differences were observed among regions, e.g., wheat production increased in northern and western Europe while Eastern and Southern Europe increased their production of fruits and vegetables (supplementary figure A 2.2.2.1.). With regards to the animal production systems, the reduction in animal numbers was lower compared to the land sparing scenario. Pigs reduced by 94%, followed by layers (reduced by 71%), broilers (reduced by 64%), beef (reduced by 56%), fish (reduced by 16%) and dairy (reduced by 15%) (supplementary figure A 4.1.1).\u003c/p\u003e \u003cp\u003eIn the land sharing scenario the intake of protein increased by 4% (83 g per person per day). Despite this increase in protein, the results do show a trend towards more plant-based proteins, with only 25% of the proteins coming from animals and 75% from plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). All nutritional requirements were fulfilled (supplementary figure A 1.2-3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe have modelled two different pathways for enhancing biodiversity within circular food systems, land sparing and land sharing, and assessed their impact on land use, GHGe emissions and dietary needs. We found that both land sharing and sparing can be applied to circular food systems with distinct but potentially positive effects on biodiversity. Both scenarios also have the potential to synergistically reduce environmental impacts, such as reducing GHG emissions. However, they require different redesigns of the food system, both in terms of production and consumption patterns. Below, we discuss some of the implications of our results for biodiversity and the food systems as well as some of the limitations of our methodology and future research.\u003c/p\u003e\n\u003ch3\u003eRedesigning the food system under land sparing and land sharing\u003c/h3\u003e\n\u003cp\u003eBoth land sharing and sparing scenarios resulted in a reduction of animal source food and an increase in plant source foods, compared to the current food system (baseline scenario), with both scenarios showing an overall reduction in animal numbers. Nevertheless, the protein intake between these scenarios in terms of quantity was different. The overall protein intake was largely reduced (to almost the minimum protein requirement) in the land sparing scenario, while within the land sharing scenario protein intake remained similar to today\u0026rsquo;s situation. The land sparing scenario\u0026rsquo;s diet is more in line with results found in previous literature of the use of circular food systems in Europe\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, depicting that land sparing will bring more intense dietary changes. The findings of higher protein (and food overall) intake in the land sharing scenario is contrasting to earlier work, which showed that reduced efficiency and lower yields in land sharing systems might increase costs and reduce food accessibility\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Our findings might point out that, while land sharing strategies reduce yield, they also help diversify crops, which might help nutrition in circular food systems, as suggested by Jenkins et al. \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe land use changes in both scenarios, but particularly in the land sparing scenario, were variable across the EU27\u0026thinsp;+\u0026thinsp;UK. In general, our results indicated that agricultural production systems mainly take place in northern and western Europe and near removal of agriculture in southern and eastern Europe, which would affect biodiversity differently across regions as well\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Additionally, that could shift economic activities and socio-economic dynamics\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, which are important factors for the implementation of such a pathway. While not the scope of this study, it should be an important aspect to consider in future research.\u003c/p\u003e\n\u003ch3\u003eThe biodiversity score\u003c/h3\u003e\n\u003cp\u003eThe biodiversity score was 86% out of the 100% in the land sharing scenario and 0% for land sparing. Our results showed that current agricultural land is enough to apply most alternative practices while producing enough healthy food within a self-sufficient European circular food system. The missing 14% of the total 100% can mainly be explained by the low inclusion of the cover crops and the need for artificial fertilizer. In practice, our findings could even underestimate the potential that more diverse cover crops could have in terms of biodiversity and nutrition if implemented in food systems\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, with only legumes being available as cover crops in the model. More diverse cropping systems, e.g. living mulches, agroforestry, offer the possibility to cover the soil whole year round to enhance biodiversity\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and also adds additional nutritional potential\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWithin the land sharing scenario, it was fertilization that mostly limited the biodiversity score. Applying organic fertilizers are quite well known to have benefits for soils and agroecosystems\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, but our results showed that they are limited in availability in line with Simon et al.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, making the model require external input of synthetic fertilizers and reducing the score. In other words, although circularity enhance the recycling of biomass, still not enough nutrients are available to fill the nutrient requirements of all crops needed to produce enough healthy food for Europe\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Additionally, there are arguments that the implementation of more extensive practices, used in land sharing, would compromise productivity or increase land use\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, which is contradicted by the findings in this paper. Nevertheless, current literature suggests that combining sparing and sharing is needed to enhance biodiversity holistically\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur findings indicate that we could almost reach full potential of an agricultural practices enhancing strategy focussing on species level, from a qualitative instead of a quantitative perspective accounting for, e.g. species abundance or richness. Note, this means biodiversity enhancement of agroecosystems encompassing both above- and below-ground taxonomic groups - a usually ignored group in models (e.g. GLOBIO\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, GLOBIOM\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, ReCiPe 2016\u003csup\u003e33\u003c/sup\u003e). Our study therefore does not indicate the number of species \u0026lsquo;affected\u0026rsquo; and if applying alternative practices would safeguard biodiversity in agricultural areas. Instead, our score should be used to indicate how to enhance biodiversity across multiple different taxa with the underlying aim to contribute to reversing biodiversity loss.\u003c/p\u003e\n\u003ch3\u003eMethodological limitations and future research\u003c/h3\u003e\n\u003cp\u003eTo interpret our results, it is important to consider some of the methodological choices made. It is important to note that the biodiversity score developed in this study does not distinguish between types of land use, i.e. we assumed that practices had the same effect in both croplands and grasslands. Grasslands are known to be usually more biodiverse than croplands\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and hence the reduction of grassland in the models could also have effects on biodiversity not considered in this model\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Additionally, the biodiversity score could be enhanced by also integrating geographic information accounting for areas with high biodiversity or high proportion of endemism (biodiversity hotspots). The main limitation to such inclusion comes from the absence of data\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Future research could benefit and require collaboration and alignment in data collection initiatives on species presence and distribution as well as in identifying species and indicators of particular interest\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and their response to agricultural practices. That would help quantify and prioritize areas to be spared and areas to be shared in terms of biodiversity and production combined.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCircularity \u0026amp; biodiversity\u003c/h2\u003e \u003cp\u003eWhile both scenarios yielded similar food system redesign, it is important to acknowledge that the type of biodiversity enhanced is significantly different and have their own set of challenges. Land use minimization, representing land sparing, is based on returning spared areas to nature. The biodiversity enhanced in this scenario is that of natural areas and not within agroecosystems. Additionally, this enhancement works on the premise that the land spared would be returned to nature and that it would not be affected by spillover effects, however, economic models and literature suggest that this might not happen unless policy/economic measures will come in place\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Applying circularity principles in the land sparing scenario, helps to reduce land use by reducing overconsumption and by recycling biomass within the food system. On the other hand, maximizing the biodiversity score, representing land sharing, would directly increase the biodiversity within agroecosystems. Applying circularity principles helps to increase the share of organic fertiliser, replacing artificial fertilisers. Furthermore, decreasing land use via e.g. recycling strategies, allows for compensating the increase in land use due to the yield reduction caused by some of the practices (e.g. no pesticide use). Without circularity, not enough land might have been available within the land sharing scenario to produce enough healthy food to the European population. Whether this will lead to enhancing biodiversity in the surrounding natural areas is not assured (e.g. due to reduced eutrophication and pesticide use) and would require more research, given existing literature on the need for a minimum size of natural areas\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCurrent literature has already shown that in terms of global biodiversity conservation, both strategies on their own would be sufficient to prevent biodiversity loss\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Our study shows the feasibility and the different configurations emerging from these two different strategies. We contribute to current knowledge by showing the contrast of these two different scenarios and their points of convergence in circular food systems. Future research could integrate these findings to more complex biodiversity interactions between the natural and agroecosystem biodiversity to model what kind of configuration would a circular food system have when using both strategies in a complimentary manner as suggested by Alkemade et al.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Social and economic implications of these changes would also need to be accounted for, as suggest by other studies\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Our results help to shed light on the different aspects policies need to engage with in regards to production and consumption for each specific scenario. Land sparing would likely require policies to insure the spared land will be returned to nature, and on the other hand, land sparing might need policies to quick start the adoption of alternative practices. Overall, our results showed that applying circularity principles will help to enhance biodiversity, either via land sharing or sparing. Moreover, despite differences in land use and diet between scenarios there were also points of convergence, such as the reduction of animal-sourced foods and increase in vegetables, depicting these scenarios are not irreconcilable.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eModelling circular food systems\u003c/h2\u003e \u003cp\u003eTo assess the potential of circularity for enhancing biodiversity we built on the CiFoS model established in van Zanten et al.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The CiFoS model is a biophysical data-driven food system optimization model coded in General Algebraic Modelling System (GAMS), spanning a wide range of aspects, starting from the land and mineral resources available in a system, going to the flow of matter and energy, e.g. nutrients that encompass the dynamics of the system, such as nitrogen addition through fertilizers or recycling of waste, to human nutritional requirements. Its generic make-up makes it applicable to different regions and aggregation of data to larger regional scales, i.e. subcontinental Europe. Currently the model is already an established tool for assessing the potential of circularity in Europe.\u003c/p\u003e \u003cp\u003eIn the current paper, we model the region of Europe, EU27\u0026thinsp;+\u0026thinsp;UK, with a total area of agricultural land available in the model of 171.7 Mha (FAOSTAT). The model uses data from SPAM\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e to define the yields and crops available in the food system and FAOSTAT data\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e for the available land and includes 43 food crops and eight fodder crops, including three different grass types (temporary, permanent and rangeland). To account for biodiversity, Europe is divided into 4 regions, the 4 sub-continental divisions of western, eastern, northern and southern Europe (see supplementary figures A), and each region is further divided into agroecological zones within them (GAEZ version 4, 33 classes). Each sub-continent/agroecological zone has land classes which are divided in croplands (temporary grasslands included), permanent pastures, and rangelands. Finally, the land classes are also divided by their soil zones, which were grouped from the original definition of the International Panel for Climate Change (IPCC) used in the model, to simpler \u0026ldquo;organic\u0026rdquo; and \u0026ldquo;mineral\u0026rdquo; soil groups.\u003c/p\u003e \u003cp\u003eThe model operates within the boundary to provide the current human population with a healthy diet, determined by caloric, macro and micro nutrient intake. For nutrient requirements, the CiFoS model accounts for the 42 most important macro- and micronutrients; and those are met combining a range of food groups (i.e. grains, tubers, vegetables, fruit, dairy, red meat, chicken, eggs, fish, legumes, nuts/seeds, oil fat, sugar and other). For the scenarios in our model, we assume a diet limited by maximum and minimum intakes as proposed by the EAT-Lancet reference range. These values are considered as constraints to be respected in the model. Greenhouse gas emissions accounted for are transportation, crops, fertilizer and livestock using the IPCC tier 1 methodology.\u003c/p\u003e \u003cp\u003eWe follow the same protocol as in Van Zanten et al.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e to create a baseline (\u0026ldquo;Agribase\u0026rdquo;) of our current food systems in the model and use that as the baseline comparison for the other scenarios where we maximize the biodiversity score (land sharing) and minimize the land use (land sparing). The baseline scenario runs on the objective function of reducing the protein supply difference from the model to the values found in the statistical data from the FAO\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, averaged between 2010 and 2019\u003csup\u003e10\u003c/sup\u003e, while respecting constraints on the land use and share of land use of crop families, and protein supply type, to determine the food system characteristics. For the full dataset see extended data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIncluding biodiversity-enhancing practices to the CiFoS model\u003c/h2\u003e \u003cp\u003eTo account for biodiversity enhancement within agricultural areas we have added five alternative agricultural practices that can be chosen by the CiFoS model and are known to enhance biodiversity across a range of taxonomic groups\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e in agroecosystems. The practices added to the model are \u0026ldquo;no tillage\u0026rdquo;, \u0026ldquo;organic fertilizer\u0026rdquo;, \u0026ldquo;no pesticide use\u0026rdquo;, \u0026ldquo;cover crops\u0026rdquo;, and \u0026ldquo;natural buffer areas\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These practices were chosen based on their known effects on multiple taxa in the agroecosystem, allowing us to use these same taxonomic groups as target for biodiversity in this model\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. For each of these practices we not only assessed how they affect biodiversity (see below \u0026ldquo;Scoring biodiversity the CiFoS model\u0026rdquo;), but also included how their application would impact other variables of the food system, e.g. yield (see \u0026ldquo;Effects of alternative practices on the food systems\u0026rdquo;), as those are key parameters in CiFoS for designing the food systems (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEffect of practices on biodiversity\u003c/h2\u003e \u003cp\u003eThis study considers biodiversity in agroecosystems from the species level and focus on taxonomic groups with known relevance for agroecosystems and studied to the five agricultural practices included in the model, as established in the literature\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The taxonomic groups considered for the biodiversity assessment in the model comprehend both aboveground biodiversity, represented by the taxa arthropods, birds and mammals, and belowground biodiversity, represented by bacteria, fungi, earthworms and nematodes. We considered the impacts of practices on biodiversity as identified in Cozim-Melges et al.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Practices identified in the literature as positive for a taxa would be assigned a \u0026ldquo;+\u0026rdquo; sign, while practices with no effect observed would be assigned \u0026ldquo;NOE\u0026rdquo;. While we had mostly complete data for how all taxa respond to each practice, some gaps existed. Practices not found to be studied for a combination practice-taxa had their \u0026ldquo;NOE\u0026rdquo; highlighted gray. We have opted to adopt a conservative approach to biodiversity enhancement with practices when data was not found. Therefore, practices with NOE and practices with no data available for that taxa were both considered neutral for biodiversity enhancement of that taxon and for that given practice. We have conducted analysis and ran the model replacing the non-available data by positive effects and by expert opinion to check for the sensitivity of the model. The changes did not affect the food system configuration or practices.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEffects of practices on the food system \u0026ndash; yield and fertilization\u003c/h2\u003e \u003cp\u003eThe five practices integrated into the model structure affected two main aspects of the model: yield (natural buffer area, no tillage, no pesticide) and fertilization type and source (organic fertilizer, cover crop). For the values used for effect of the practices on the model, we have used literature data and expert knowledge from both the Farming Systems Ecology and Crop System Analysis groups in Wageningen University, coherent with the European context. We have opted to use the most conservative values when no data was available, i.e. considering absence of data as no effect, for how these alternative practices impact yield or fertilization, in order to avoid overshooting our results. For the specific case of no pesticide use limited data were available, and hence we have extrapolated the values modelled for a reduction of 50%, as per the EU 30 agenda\u0026rsquo;s ambitions, to simulate our coefficients of loss for the complete absence of pesticides. The R script and the dataset can be found in the GIT. A full description of the equations and values used based on the literature can be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and table, values for each coefficient used can be found in supplementary material C.\u003c/p\u003e \u003cp\u003eThese effects were added directly to the dataset as additional options through the use of R script\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The GAMS model would then consider all these options in the dataset, with both different biodiversity scores available for the combination of practices and different yields and sources of fertilization, and choose the optimal solution to maximize biodiversity while respecting the constraints of a healthy diet and maximum land available.\u003c/p\u003e \u003cp\u003eTable 2. Definition of practices, their effects and data sources used \u003c/p\u003e \n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58894_9946feeafa4c1df7/58894_custom_files/img1733734275.png\" width=\"803\" height=\"798\"\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eScoring biodiversity in the CiFoS model\u003c/h2\u003e \u003cp\u003eTo assess biodiversity enhancement we developed a biodiversity score which is defined as the potential impact the adoption of certain agricultural practice(s) to biodiversity of the food system. This scoring system is based on the impact the alternative practices mentioned above have on the biodiversity of the target taxonomic groups in agroecosystems. Using the matrix in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, we have translated all positive signs as a positive point of a practice to that given taxa, while all \u0026ldquo;NOE\u0026rdquo; as no points. Hence the potential of a practice to enhancing biodiversity is given as the sum of all taxa positively affected by that practice, as seen in equation (I), where p refers to the practice.\u003c/p\u003e \u003cp\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Score\\left(p\\right)=\\:{\\sum\\:}_{practice}Score\\left(taxa\\right)$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eSome practices were studied for more taxa than others, while some taxa were positively affected by more practices. Thus, evaluating biodiversity by comparing the individual taxa affected would not be a correct comparison, given their different maximum and minimum scores. We have the extended values in the model for the contribution to the score of each hectare of each practice and taxa (supplementary material B), but have maximized and reported a unified score across taxa for a practice.\u003c/p\u003e \u003cp\u003eThe calculation of the biodiversity score can be seen in equation (II), where, \u003cem\u003eBioS\u003c/em\u003e is the biodiversity score(percentage), \u003cem\u003ec\u003c/em\u003e and \u003cem\u003eaez\u003c/em\u003e are both the sub-continent and the agroecological zone (measured in ha), and \u003cem\u003ep\u003c/em\u003e is the practice. We simplified potential interactions and considered a linear interaction, i.e. the more positive practices enhancing taxa are applied, the higher the likelihood that biodiversity will be enhanced. Given all effects are not measured as magnitude, a higher score does not represent magnitude of effect, but rather the number of practices with positive effects across all taxa being used in the model.\u003c/p\u003e \u003cp\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:BioS\\left(c,\\:aez\\right)=\\:\\frac{\\frac{{\\sum\\:}_{c,\\:aez}Score\\left(p\\right)*area\\left(p\\right)}{Tot\\:Area\\:\\left(c,aez\\right)}}{Max\\:Biodiv\\:Score\\:}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe biodiversity score is given then per hectare and is the total sum of the scores of the practices applied in a an agroecological zone inside each sub-continent divided by the total land use of that area, and represents the potential to enhance biodiversity across these seven taxa per hectare. Hence, our biodiversity score is a metric of the potential for biodiversity enhancement in comparison with current systems. The final biodiversity score given by the model is given per sub-continent and a weighted average of the biodiversity score for Europe is also calculated.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u0026nbsp;\u003c/strong\u003eThis project received funding from the AVINA foundation (www.circularfoodsystems.org). We would like to thank Laura Gerwien and Thomas Maindl with regards to model advice.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration.\u003c/strong\u003e The authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions.\u0026nbsp;\u003c/strong\u003eFM (Felipe Cozim Melges) and HvZ (Hannah H.E. van Zanten, devised the idea for the study. All authors helped strengthen the methodology of the paper and reviewed the results. FM and HvZ wrote the main manuscript text. All authors contributed to, reviewed and approved the final manuscript submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability.\u003c/strong\u003e All additional model data is available in the supplementary materials. Model information can be requested directly with the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability.\u003c/strong\u003e Custom R scripts developed for the analyses and visualizations in this manuscript and the raw data have been deposited in the GIT repository and are available on request under a licence.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCampbell, B. M., Beare, D. J., Bennett, E. M., Hall-Spencer, J. M., Ingram, J. S. I., Jaramillo, F., Ortiz, R., Ramankutty, N., Sayer, J. A., \u0026amp; Shindell, D. (2017). 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Trends in Ecology \u0026amp; Evolution, 36(10), 919\u0026ndash;930. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tree.2021.06.010\u003c/span\u003e\u003cspan address=\"10.1016/j.tree.2021.06.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Agroecosystems, circularity, food systems, Biodiversity, land-sparing, land-sharing, CiFoS model","lastPublishedDoi":"10.21203/rs.3.rs-5261909/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5261909/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFood systems are largely responsible for today\u0026rsquo;s anthropogenic environmental impacts. Transitioning towards a circular food system is seen as a promising solution to reduce land use (LU) and greenhouse gas emissions (GHGe). But what about biodiversity? The aim of this paper was to assess the potential of enhancing biodiversity in circular European food systems. Two scenarios were assessed with a food systems optimization model: land sharing or sparing while producing healthy food. Our results show that both scenarios can enhance biodiversity while reducing GHGe. The land sparing scenario reduced LU by 81%, depicting great potential for rewilding. However, reduction of LU was achieved via intensification, decreasing agroecosystem\u0026rsquo;s biodiversity (0 biodiversity score). Conversely, land sharing increased biodiversity in agroecosystems (86% biodiversity score), and LU was maintaned. Both scenarios require to radically redesign today\u0026rsquo;s food system. Our results demonstrate circular food systems can help enhance biodiversity via land sparing or sharing.\u003c/p\u003e","manuscriptTitle":"Enhancing biodiversity with circular food systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-09 09:05:37","doi":"10.21203/rs.3.rs-5261909/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":"1c52942b-ece0-47da-8658-9c8f6175874b","owner":[],"postedDate":"December 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41203551,"name":"Earth and environmental sciences/Ecology/Agroecology"},{"id":41203552,"name":"Earth and environmental sciences/Ecology/Biodiversity"},{"id":41203553,"name":"Scientific community and society/Agriculture"}],"tags":[],"updatedAt":"2025-01-24T15:45:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-09 09:05:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5261909","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5261909","identity":"rs-5261909","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}