25 Oct 2016

Using the Direct Land Use Change Assessment Tool and Agri-footprint crop model to understand trends

Poster presentation at LCA Food 2016 Dublin

Figure 1. Area harvested of 4 oil crops from 1985 to 2030 (projected). As can be seen in the figure, all four crops are expanding, however the rate of expansion and the steadiness of the expansion differs.

Introduction

Blonk Consultants has developed tools to generate life cycle inventory data on crops for Agri-footprint1 and to generate data on the emissions of land use change due to crop cultivation2.

The research question is two-fold:

  • How do choices on time-scales affect the overall climate change impact?
  • How did the climate change impact of oil crops develop over time, and how could they develop in the future?

 

Methods

For the expansion of crop areas (and potential related reduction of forest area) the time horizon used plays an important role. An often used timeframe to determine the change of land is 20 years, which somewhat arbitrarily chosen. Our analysis shows that the relative expanded crop area3 (an important element in determining the LUC emissions), is indeed dependent on the timeframe, but leads to different results for the different crops.

Results

All four crops analysed show an increase in area harvested (figure 1), however specific aspects of the growth influence the calculated impact from land use change (figure 2 and 3).

  1. For oil palm fruit from Indonesia, in the longer time frames, nearly all of the crop is essentially considered to have been expanded and the impact mainly depends on the 'year divider', so the impact decreases with longer timeframes (figure 2a).
  2. For soybeans from Brazil and Argentina, the principle is similar, but the data is more variable, which makes the outcome more dependent on the reference year.
  3. For rapeseed in France, overall there has been a linear increase, however with larger year-to-year changes. The time frame has relatively little influence, and more important is the question on how to deal with the yearly fluctuations (figure 2b).

The relative importance compared to other emissions that have an effect on global warming varies (figure 3). Also the variability related to emissions from land use change varies (error bars in figure 3).

 

Figure 2. Direct land use change (LUC) impacts for oil palm from Indonesia (2a) and rapeseed from France (2b) calculated with 3 different timeframes (10 years, 20 years = default and 30 years) and taking into account future crop expansions in 2020 and 2030.
Figure 3. Climate change impact of 4 oil crops including direct land use change based on a 20 yr timeframe including uncertainty based on a 10 yr timeframe (maximum) and a 30 yr timeframe (minimum). And using baseline years 2009-2013.

Conclusions

LCIA are sensitive for products when direct LUC is included. It is advised to conduct a sensitivity analysis. This sensitivity analysis could be performed by using different timeframes instead of only 20 years as default. This options is available in the upcoming version of the LUC tool.

Limitations

Please note that the figures shown in this paper are not meant as comparative assertion because a functional unit of 1 kg crop is used; differences in nutrients, oil yield, co-products are not taken into account.

References

1 www.blonksustainability.nl/agri-footprint
2 www.blonksustainability.nl/LUCimpact
3 Soybean projections based on Masuda, T & Goldsmith, P (2009) World Soybean Production: Area Harvested, Yield, and Long-Term Projections. Int. Food and Agribusiness Management Review. Palm oil and rapeseed expansion based on linear extrapolation.

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