Unveiling the potential for an efficient use of nitrogen along the food supply and consumption chain

Ensuring global food security is one of the challenges of our society. Nitrogen availability is key for food production, while contributing to different environmental impacts. This paper aims firstly to assess nitrogen flows and to highlight hotspots of inefficient use of nitrogen along the European food chain, excluding primary production. Secondly, it aims to analyse the potential for reducing the identified inefficiencies and increase nitrogen circularity. A baseline and three scenarios-reflecting waste targets reported in EU legislation and technological improvements- are analysed. Results highlighted a potential to reduce reactive nitrogen emissions up to more than 45%. However, this would imply the conversion of reactive nitrogen in molecular nitrogen, such as urea, before re-entering in the food chain. Techniques to harvest reactive nitrogen directly from urine and wastewater are considered promising to increase nitrogen use efficiency along the food chain.


Nitrogen of food products and food waste
The estimation of N content of food products in primary production stage is based on protein content and on protein-to-N conversion factors given in USDA food composition database (https://ndb.nal.usda.gov/ndb/). Protein content is taken from several sources: • For cereals, vegetables, potatoes, oil crops and milk: CAPRI model data (Britz and Witzke, 2014;Carmona-Garcia et al., 2017). • For meat, CAPRI model data (Britz and Witzke, 2014;Carmona-Garcia et al., 2017).
For the calculation of N content in food products and food waste in the following stages, some assumptions are taken on the fractions of the different product types which are discarded. These assumptions are detailed in Table 1. 1 Non consumed for food or for any other uses. It is mainly manure. We consider pet food as part of the food, as they are products bought and consumed in households. 2 Category 1 and 2 rendered fat and protein meal (not usable for human consumption due to sanitary reasons). It does not include other rendering products used for other non-food purposes. 3 Slaughterhouse and rendering products used for human consumption (as fresh or used in the food industry): meat, organs and fat, pet food and other fresh or rendered products used in food industry, such as gelatine, bone meal, meat meal, feather meal, bone meal, blood meal, rendered fat. 4 Fresh milk products, whole milk powder, skimmed milk powder, cheese, butter, casein, whey powder and concentrated milk. 5 Fresh milk products, whole milk powder, skimmed milk powder, cheese, butter and cream. 6 Cow and sheep milk, fresh milk products, whole milk powder, skimmed milk powder, cheese, butter, cream, casein, whey powder and concentrated milk. 7 According to literature (https://www.eggindustrycenter.org/media/cms/2014_1_VanHorne_EUEconomicsPerspect_D576964DB61F8.pdf), egg processing sector accounts for 26% of egg market (EU-28, 2014). We assume that 26% of eggs are without shell and the remaining 74% of the egg in the market is the entire egg. 8 Taken from CAPRI cereal module; it is composed partly of white flour, partly wholemeal flour and some addition of bran in cerealbased products. The share of refined flour depends on the specific cereal. 9 We assume that waste in the processing sector corresponds to peels of fruits that will be transformed into juice. In the distribution sector, we assume that 80% of the fruit in the market is fresh food and 20% is in the form of juice (https://research.rabobank.com/far/en/sectors/regional-food-agri/world_fruit_map_2018.html), while waste in the households corresponds to peels removed from fruits. N composition takes into account N content of peels and peel weights as compared to the whole fruit.

Nitrogen in excrements
A part of the consumed food and drink is transformed into human excrements (feces + urine).
Regarding the nitrogen from food, only a small part remains in the body, all other is excreted. The amount of nitrogen excreted after human metabolism was subdivided into fractions contained in urine and in feces and calculated based on data from Rose et al. (2015). Rose et al. (2015) investigated human excrements from various geographical locations and provided key parameters for the design of wastewater facilities (Rose et al. 2015;page 1862). intake was reported to be the predominant reason for the variation in concentrations with the minimum in cases of absence of proteins in food (Rose et al. 2015(Rose et al. , page 1852. The urine to feces ratio is 10.9 for wet and 2.0 for dry substance amounts.  A -calculated from N mass and dry mass (Table 2) B -calculated from N mass and wet mass (Table 2) In feces, the predominant nitrogen compound is protein. Rose at al. (2015; page l862) provided a protein value of 6.3 g/cap & d as design parameter (conversion factor from N into protein: 6.25).
Further N containing compounds in feces include ammonia and nitrite (Rose at al., 2015;page 1841).
In urine, of the nitrogenous fractions urea is the predominant, making up between 75% and 90% (Lentner, 1981;cited in Rose et al., 2015cited in Rose et al., , page 1852. Creatinine is a further significant nitrogenous fraction in urine, whereas nitrate concentrations are low (Rose et al., 2015(Rose et al., , page 1852.

Nitrogen compounds emissions from incineration
N compounds emissions depend on the type of DeNOx technology used in the incineration plant.
The two most diffused technologies in the EU are selective non-catalytic reduction (SNCR) and selective catalytic reduction (SCR). The emission factors for nitrogen compounds emissions were determined by considering a share of 25% for SNCR, 43% for SCR high-dust, and 32% for SCR lowdust as reported by Doka (2017). The conversion efficiency of NOx for these two technologies were respectively 60% for the SNCR and 85% for SCR (both high-dust and dust). Table 4 reports an overview of the N embedded, Nr emissions and N2 emissions in the different scenarios analysed.  Some of the data used as input data to the calculations are dependent on context-specific conditions.
In order to take into consideration the effects of such variability, a sensitivity analysis was performed by assuming a 20% variation compared to the average values of the following parameters: the share of collected food waste and wastewater, the efficiency of wastewater treatments, the emissions from wastewater treatments, and N recovery. Table 5 reports an overview of the input data considered in the sensitivity analysis. Table 6 reports an overview of the results of the sensitivity analysis in terms of variation of N embedded and N emitted compared to average results and based on values obtained from input parameters changes, the minimum corresponding to -20% of the average input values and maximum to +20% of the average input values.