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Richard Petrasek1, Thomas Drapela1, Thomas Lindenthal1, Isabella Gusenbauer1, Stefan Hörtenhuber1, Ruth Bartel-Kratochvil1, Michaela C. Theurl1,22425

Key words: Sustainability assessment, carbon footprint, water footprint, biodiversity assessment, regional benefit, organic products

Abstract

This contribution presents the assessment of four aspects of sustainability, product carbon footprint (PCF), water footprint (WF), biodiversity potential (BDP), and regional benefit (RB) along the value chain of 79 food products for an Austrian organic brand. Only PCF and WF were highly correlated (r=0.92, p<0.001) and showed much more variation than BDP and RB. A principal component analysis (PCA) was conducted to examine similarities and differences of five aggregated food product groups – 1. cereals & legumes, 2. fruits, 3. vegetables, 4. dairy products 5. meat & eggs. The products and product groups mainly differentiated along the first component that was largely determined by PCF and WF. Our analyses show that products can perform very differently in the analysed sustainability aspects. We conclude that the isolated assessment of single sustainability aspects is not sufficient and a comprehensive method covering all important aspects of sustainability would be required.

Acknowledgments

The project was funded by Hofer KG.

Introduction

Sustainable food production is an important issue in the scientific and public debate. Consumers’ awarenessof sustainable food production is increasing andfarmers, processors and retailers step up their effortsto communicate their sustainability performance.

In the sustainability assessment of food products, we focused on four aspects of major importance: greenhouse gas emissions (GHGE), water use, biodiversity potential and regional benefit. Considering a set of different aspects of sustainability is preferable compared to single aspect approaches (e.g. only GHGE) because products might perform differently in different aspects. Livestock products, for example, might show high regional benefits and may be positive for biodiversity but cause high GHGE. These patterns can vary considerably for different products. For this paper, we assessedthe above-mentioned sustainability aspects for five product groups: i) cereals and legumes, e.g. oat, soya; ii) fruits, e.g. raspberry, apple; iii) vegetables, e.g. potatoes, pumpkins; iv) dairy products, e.g. milk, cheese and v) meat and eggs, e.g. beef, minced meat.

Material and methods

The assessment methods for the four considered aspects of sustainability have been developed for organic products in Austria between 2008 and 2015.

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Research Institute of Organic Agriculture (FiBL). http://www.fibl.org/de/oesterreich/standort-at.html, [email protected]

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Scientific Track “Innovative Research for Organic Agriculture 3.0” 19th Organic World Congress, New Delhi, India, November 9-11, 2017 Organized by ISOFAR, NCOF and TIPI

The PCF includes all relevant greenhouse gases (Carbon Dioxide, CO2; Methane, CH4; Nitrous

Oxide, N2O) in CO2-equivalents (CO2eq), according to IPCC (2006) and IPCC (2007) guidelines,

and is closely based upon the eco-balance guidelines ISO 14040, ISO 14044 and PAS 2050 standard. The system boundaries range from agricultural production to retailers, including the upstream supply chain (e.g., production of fertilizer, pesticides or seeds) as well as processing, packaging, storage and all transports up to and including retail. Unlike most PCF found in the literature, two further items, namely, land use change (LUC; GHGE-source) and changes in humus (GHGE – source or -sink) were included in the analysis based on Hörtenhuber et al. (2010), with minor modifications according to Küstermann et al. (2008). Secondary data from GEMIS Austria, ecoinvent and relevant national and international publications and statistics were consulted (e.g. Fritsche et al., 2007; Nemecek and Kägi, 2007).

The WF results are based on the method presented by Hörtenhuber et al. (2014), which refers to the concept of Hoekstra et al. (2010). The WF includes a use of (a) surface- and groundwater (so-called „blue“ water), (b) „green“ evapotranspiration water from precipitation and (c) virtual „grey“ water. The latter is the amount of water which is potentially needed to dilute emissions (e.g. nitrate) into freshwater below limits for drinking water quality. The results are calculated in regard to life cycle assessment (LCA) principles and include all stages along the supply chain from cradle to retailer. Results are accounted for based on an inventory level, i.e. they were not weighted to generate for instance estimates on critical demands for scarce water resources (see Hörtenhuber et al., 2014). The BDP estimates how “biodiversity friendly or – promoting” an agricultural farm is managed and covers the entire farm (Schader et al., 2014). The core of the assessment method consists of 99 parameters concerning agricultural practices and semi-natural habitats and their impacts on the diversity of eleven indicator species groups. For each farm a biodiversity performance score, the so- called biodiversity potential, is calculated ranging from 0% to 100%, where 100% would be reached with the highest possible scores for all parameters.

The RB indicates the socio-economic advantages a region gains by regionally produced food, with the background of resilience and sustainable development. By addressing a food product, the model comprises its whole supply chain covering agricultural production, processing and retailing. A set of 28 indicators, derived from literature and clustered to the four themes “regional value-added”, “regional resilience”, “corporate resilience” and “product properties" forms the core element of the model. Input data values are matched with their corresponding weights and impacts, and are multiplied by the so called "Regional Correlation Factor" (for details see Markut et al., 2015) which results in an interim score for each input data value. These interim scores are summed up and result in the regional benefit induced by the regionally labelled product for the respective region.

For this paper, we compiled 79 products results and categorized them into the five product groups. Pairwise Pearson correlations between the results of the four sustainability aspects were calculated. We conducted a PCAto analyse the relations between the 79 products and the five product groups regarding all four assessed sustainability aspects. All statistical analyses were performed with PAST 3.12 (Hammer et al., 2001).

Results

The results of the four aspects of sustainability for the 79 assessed products, aggregated in five groups, are shown in Figure 1. The PCF and the WF, which are strongly correlated (r = 0.92, p < 0.001), have the absolute highest values for animal products (mean PCF = 4123 g CO2eq / kg

product) in contrast to vegetable products (mean PCF = 160 g CO2eq / kg product). Since PCF and

WF are calculated per kg product, differences in absolute yields have a substantial effect on the results. The BDP scores show less variation between groups, although variation within groups was considerablyhigh in some groups, especially vegetables. The farms themselves, the on farm practice

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Organic World Congress 2017 in New Delhi, India, November 9-11, 2017

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and the ecological measures (e.g. no pesticide use), were crucial for the achieved results. The RB results indicate slightly higher values for animal products as well as for fruits and their value chains.

Figure 1. Results for the four sustainability aspects – product carbon footprint (A), regional benefit (B), water footprint (C) and biodiversity potential (D) – of the product groups CER (cereals &

legumes, n = 18; for RB n = 8), FRU (fruits, n = 4), VEG (vegetables, n = 34); DAI (dairy products, n = 17) and MEA (meat & chicken eggs, n = 6). Please note the different ranges on the Y-axis in A and C for CER, FRU and VEG and for DAI and MEA, respectively.

Figure 2. PCA-Scatterplot (correlation matrix, eigenvalue scale). Component 1 (51.0% explained variance) is mainly correlated with product Carbon Footprint and water footprint with loadings of 0.66 each. Component 2 (26.5% explained variance) is correlated with regional benefit (loading -0.59) and biodiversity potential (loading 0.73). Note: Axis not centered on zero.

In the PCA,the products and product groups mainly differentiated along the first component (51.0% explained variance, Fig.2) that was mainly determined by PCF and WF (both loadings of 0.66). The second component represents mainly BDP (0.73) and RB (-0.59). Visual inspection of the scatter plot reveals two groups of products, which are separated from the others: i) products with high

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 -2 -1 0 1 2 3 4 5 6 7 Co m po ne nt 2 Component 1

CEREALS & LEGUMES FRUITS

VEGETABLES DAIRY MEAT & EGGS

Scientific Track “Innovative Research for Organic Agriculture 3.0” 19th Organic World Congress, New Delhi, India, November 9-11, 2017 Organized by ISOFAR, NCOF and TIPI

values of RB combined with average values of PCF and WF for vegetable products; ii) high values of BDP with average values of PCF and WF for animal products (in particular for dairy products).

Discussion

This work gives a first detailed overview of four sustainability performance indicators of different organic agricultural food products – over the whole value chain (PCF, WF, RB) and at the farm level (BDP). The consideration of PCF, WF, RB and BDP covers the main local and global issues of the present and the future of sustainable agricultural production and its provision of public (e.g. biodiversity) and private (e.g. food, fibres) goods. To understand such systems and to optimize them, detailed examinations combined with a comprehensive assessment of sustainability issues are needed. Some animal products, for example, contribute to relatively high regional benefits and to conservation and generation of biodiversity on farmland, but consume high amounts of resources with respective impacts on PCF and WF. Single parameters (even when including different aspects in one model like RB) are only aspects of sustainability and do not cover the whole picture. However, the detailed examination with such methods will help to tackle present and future challenges (e.g. consumption and availability of means of production or rather resources) of our food system.

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