year <- 2017
tocc <- 0.30 #occupation time in the year
type <- "SOYBEANS"
save_loc <- "C:/Users/michael.lathuilliere/Desktop/"

Purpose of this document

This document summarizes the results for a 4 page paper to be submitted for LCA Food 2020 before July 1st. This paper will be peer-reviewed and then added to the book of abstract of the conference later in the year (virtual conference Oct 13-16 2020).

The results aim to show the combined supply chain of Brazil, Argentina and Paraguay soybean exported to China (Mainland), the EU and France in 2017. The study is based on life cycle assessment (LCA) with a focus on biodiversity impacts of land occupation using the impact factors of Chaudhary and Brooks (2018). The functional unit is 1 tonne of soybean (not cake, nor oil) sourced from specific ecoregions in Brazil, Argentina and Paraguay and exported to China (Mainland), the EU and France in 2017.

First, we derive the commodity supply mix (CSM) based on each country’s ecoregion source composition for 1 tonne of soybean is calculated using the Trase supply chain maps:

Source jurisdictions in ecoregions of South America were classified using the “majority soy” rule, meaning that the jurisdiction was labeled as being part of an ecoregion if the majority of the soybean cropland identified in that jurisdiction was in the ecoregion (using crop maps from GLAD - University of Maryland). This rule applies to jurisdictions located at the border of two or more ecoregions.

When separating the destination countries into China, the EU and France we find the following % unknown in 2017:

Step-by-step procedure to derive the Commodity Supply Mix

  1. Derive the total exports of each trade partner (China, EU, France) in tonnes (from the export data) and hectares (using yield at the jurisdiction level from which soybean was sourced)
  2. Derive the total exports of each trade partner per ecoregion, in tonnes and hectares (following 1.)
  3. Obtain hectares per tonne for each destination country and source ecoregion (from 2.) - the result is in hectares(ecoregion) per tonne(ecoregion)
  4. Obtain the percentage of soy exported by each ecoregion for each country of destination - the result is tonne(ecoregion) per tonne(export)
  5. Derive the portion of ecoregion per tonne of soy exports multiplying 3. and 4. to obtain the life cycle inventory of land occupation (converting ha into m2)

Biodiversity impact using characterization factors from Chaudhary and Brooks (2018)

We apply the Crop Intensive characterization factors from Chaudhary and Brooks (2018).

We also compare the results against the national average, meaning the average land occupation and biodiversity impact that would have been calculated in a situation where the sub-national sourcing was not available. This calculation is carried out as follows in 2017 (characterization factors are as per Chaudhary and Brooks (2018)):

  • Brazil: 2960 m2 ton-1 (from IBGE) as land occupation and a characterization factor for Brazil of 2.44 10-13 PDF m-2
  • Argentina: 3505 m2 ton-1 (from M of Agro), and a characterization factor for Argentina of 1.51 10-11 PDF m-2
  • Paraguay: 4780 m2 ton-1 (from official statistics - check reference), anda characterization factor for Paraguay of 1.22 10-11 PDF m-2

Note that the land occupation LCI is area*occupation time; we take occupation time tocc = 0.3 year

The above are the cumulative life cycle inventory and biodiversity impact of land occupation considering the mean biodiversity impact factors for each ecoregion (Chaudhary and Brooks, 2018) shown as a dashed line.

Density functions

The graphs below show the density of biodiversity impacts (on log scale) determined for each export of soy from a jurisdictions in South America to either China (Mainland), the EU or France. Results are provided for the impacts as mean (dashed line), and 95% interval (green = U95, red = L95%) from the characterization factors as defined in Chaudhary and Brooks (2018).

These results may be interpreted as an answer to the question “What is the greatest probability of biodiversity impact given the uncertainty in exports to the given country”. What I mean here is in the event that an analyst does not know the exact location of the soybean used in his/her LCA, he/she can determine a probability of largest impact based on the CSM.

Reference Cited

Chaudhary and Brooks (2018) Land Use Intensity-specific Global Characterization Factors to Assess Product Biodiversity Footprints. Environmental Science and Technology 52(9): 5094-5104, doi: 10.1021/acs.est.7b05570