Beef and coal are key drivers of Australia’s high nitrogen footprint

People have increased the amount of reactive nitrogen (Nr) in the environment as a result of food production methods and consumption choices. However, the connection between dietary choices and environmental impacts over time has not yet been studied in China. Here we combine a nitrogen footprint tool, the N-Calculator, with a food chain model, NUFER (NUtrient flows in Food chains, Environment and Resources use), to analyze the N footprint of food in China. We use the NUFER model to provide a detailed estimation of the amounts and forms of Nr released to the environment during food production, which is then used to calculate virtual nitrogen factors (VNFs, unit: kg N released/kg N in product) of major food items. The food N footprint consists of the food consumption N footprint and food production N footprint. The average per capita food N footprint increased from 4.7 kg N capita À1 yr À1 in the 1960s to 21 kg N capita À1 yr À1 in the 2000s, and the national food N footprint in China increased from 3.4 metric tons (MT) N yr À1 in the 1960s to 28 MT N yr À1 in the 2000s. The proportion of the food N footprint that is animal-derived increased from 37% to 54% during this period. The food production N footprint accounted for 84% of the national food N footprint in the 2000s, compared to 62% in the 1960s. More Nr has been added to the food production systems to produce enough food for a growing population that is increasing its per-capita food consumption. The increasing VNFs in China indicate that an increasing amount of Nr is being lost per unit of N embedded in food products consumed by humans in the past five decades. National N losses from food production increased from 6 MT N yr À1 in the 1960s to 23 MT N yr À1 in the 2000s. N was lost to the environment in four ways: ammonia (NH 3 ) emissions and dinitrogen (N 2 ) emissions through denitrification (each account for nearly 40%), N losses to water systems (20%), and nitrous oxide (N 2 O) emissions (1%). The average per capita food N footprint in China is relatively high compared with those of developed countries in the 2000s. To reduce the food N footprint in China, it is important to both reduce the Nr losses during food production and encourage diets associated with a lower N footprint, such as shifting towards a more plant-based diet.

Section: Comparison With Other Countriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How China’s nitrogen footprint of food has changed from 1961 to 2010

Guo

Chen

Bai

et al. 2017

“…The food N footprint of oil palm products calculated in the present study was compared with previously reported N footprints of several countries and the world average (Gu et al 2013, Pierer et al 2014, Shibata et al 2014, Stevens et al 2014, Liang et al 2016, Oita et al 2016, Hutton et al 2017, Shindo and Yanagawa 2017, Hayashi et al 2018 (table 3). The food N footprint of oil palm products in these countries ranged from 0.07 to 0.18 kg N capita -1 yr -1 , whereas their food N footprint ranged from 8.9 to 32 kg N capita -1 yr -1 .…”

Section: Food Nitrogen Footprint Of Oil Palm Productsmentioning

confidence: 86%

Concealed nitrogen footprint in protein-free foods: an empirical example using oil palm products

Hayashi

Oita

Nishina

2020

The agro-food system satisfying human food demand releases heavy nitrogen (N) loads into the environment. The N footprint is an indicator of N loads from individual consumption of food as well as energy. A bottom-up approach called the 'N-calculator method' calculates the food N footprint using the N content in consumed foods, such that the N footprint of protein-free foods is treated as zero. This method underestimates the N footprint of protein-free foods, such as oil and sugar, when the source crops require N input in production. In this study, we propose a substitution factor, the virtual nitrogen factor for protein-free foods (VNFree), defined as the potential N load per unit weight of consumed food, to explicitly calculate the production N footprint. Oil palm and its products, palm oil (PO) and palm kernel oil (PKO), were chosen for this case study of protein-free foods. Global mean VNFree values of PO and PKO obtained by averaging national-scale data of the three countries with the largest production (Indonesia, Malaysia, and Thailand) were 0.0241 and 0.0037 kg N kg -1 oil, respectively. The 6.5-times difference in VNFree values was attributed to the difference in oil yield. The food N footprint of PO and PKO calculated here represented less than 2% of the previously reported total food N footprints of several countries. However, oil palm products are also used for industry, and the chemical fertilizer consumption for oil palm accounted for only 8%-12% of that of all oil and sugar crops. The protein-free N footprint of all these products will be much larger. We expect that the current N-calculator method as a bottom-up approach will be improved by expanding the VNFree concept, which enables the calculation of the concealed N footprint in protein-free products, including all uses of oil and sugar crops.

“…For example, the consumer-oriented N-Calculator model by Leach et al (2012) estimates the reactive N released to the environment on a per capita basis related to food production and consumption, household utilities, transportation, and the purchase of goods and services. National per capita N footprints have been calculated for more than 10 countries and vary between 15 and 47 kg N capita −1 yr −1 (Pierer et al 2014, Shibata et al 2014, Stevens et al 2014, Liang et al 2016, Shibata et al 2017, Guo et al 2017, Elrys et al 2019. Typically, these studies have emphasized the role of food production (e.g.…”

Section: Introductionmentioning

confidence: 99%

Geographic versus institutional drivers of nitrogen footprints: a comparison of two urban universities

MacDonald

Talbot

Moore

et al. 2020

Excess reactive nitrogen (N) is linked to a myriad of environmental problems that carry large social costs. Nitrogen footprint tools can help institutions understand how their direct and indirect activities are associated with N release to the environment through energy use, food, and transportation. However, little is known about how geographic context shapes the environmental footprints of institutions. Defining the system boundaries over which institutions are responsible and able to control individual drivers of N footprints is also a challenge. Here, we compare and contrast the circa 2017 N footprints for two research intensive universities located in Montréal, Canada, with a combined full-time equivalent campus population of ∼83 000. Our estimate of McGill University's N footprint (121.2 t N yr −1 ) is 48% greater than Université de Montréal's (74.1 t N yr −1 ), which is also reflected on a per capita basis (3.3 and 1.6 kg N capita −1 yr −1 , respectively). Key institutional factors that explain the differences include McGill's larger residential and international student populations, research farm, and characteristics of its on-campus fuel use. We use a series of counterfactual scenarios to test how shared urban geographic context factors lead to an effective reduction of the N footprints at both universities: the relatively small direct role of both institutions in food intake on campus (29%-68% reduction compared to a counterfactual scenario), energy from hydroelectricity (17%-21% reduction), and minimal car commuting by students (2%-3% reduction). In contrast, the nearzero N removal from the municipal wastewater system effectively increases the N footprints (11%-13% increase compared to a modest N removal and offset scenario). Our findings suggest that a shared geographic context of a dense city with plentiful off-campus housing, food options, and access to hydroelectricity shapes the absolute N footprints of Montréal's two main universities more than the divergent institutional characteristics that influence their relative N footprints.