LCA of soybean meal

Dalgaard R, Schmidt J, Halberg N, Christensen P, Thrane M, Pengue W A (2008)

Publication info

International Journal of Life Cycle Assessment, 13(3):240-254


Background, Aim and Scope

Soybean meal is an important protein input to the European livestock production, with Argentina being an important supplier. The area cultivated with soybeans is still increasing globally, and so are the number of LCAs where the production of soybean meal forms part of the product chain. In recent years there has been increasing focus on how soybean production affects the environment. The purpose of the study was to estimate the environmental consequences of soybean meal consumption using a consequential LCA approach. The functional unit is ‘one kg of soybean meal produced in Argentina and delivered to Rotterdam Harbor’.

Materials and Methods

Soybean meal has the co-product soybean oil. In this study, the consequential LCA method was applied, and co-product allocation was thereby avoided through system expansion. In this context, system expansion implies that the inputs and outputs are entirely ascribed to soybean meal, and the product system is subsequently expanded to include the avoided production of palm oil. Presently, the marginal vegetable oil on the world market is palm oil but, to be prepared for fluctuations in market demands, an alternative product system with rapeseed oil as the marginal vegetable oil has been established. EDIP97 (updated version 2.3) was used for LCIA and the following impact categories were included: Global warming, eutrophication, acidification, ozone depletion and photochemical smog.


Two soybean loops were established to demonstrate how an increased demand for soybean meal affects the palm oil and rapeseed oil production, respectively. The characterized results from LCA on soybean meal (with palm oil as marginal oil) were 721 gCO2 eq. for global warming potential, 0.3 mg CFC11 eq. for ozone depletion potential, 3.1 g SO2 eq. for acidification potential, −2 g NO3 eq. for eutrophication potential and 0.4 g ethene eq. for photochemical smog potential per kg soybean meal. The average area per kg soybean meal consumed was 3.6 m2year. Attributional results, calculated by economic and mass allocation, are also presented. Normalised results show that the most dominating impact categories were: global warming, eutrophication and acidification. The ‘hot spot’ in relation to global warming, was ‘soybean cultivation’, dominated by N2O emissions from degradation of crop residues (e.g., straw) and during biological nitrogen fixation. In relation to eutrophication and acidification, the transport of soybeans by truck is important, and sensitivity analyses showed that the acidification potential is very sensitive to the increased transport distance by truck.


The potential environmental impacts (except photochemical smog) were lower when using rapeseed oil as the marginal vegetable oil, because the avoided production of rapeseed contributes more negatively compared with the avoided production of palm oil. Identification of the marginal vegetable oil (palm oil or rapeseed oil) turned out to be important for the result, and this shows how crucial it is in consequential LCA to identify the right marginal product system (e.g., marginal vegetable oil).


Consequential LCAs were successfully performed on soybean meal and LCA data on soybean meal are now available for consequential (or attributional) LCAs on livestock products. The study clearly shows that consequential LCAs are quite easy to handle, even though it has been necessary to include production of palm oil, rapeseed and spring barley, as these production systems are affected by the soybean oil co-product.

Recommendations and Perspectives

We would appreciate it if the International Journal of Life Cycle Assessment had articles on the developments on, for example, marginal protein, marginal vegetable oil, marginal electricity (related to relevant markets), marginal heat, marginal cereals and, likewise, on metals and other basic commodities. This will not only facilitate the work with consequential LCAs, but will also increase the quality of LCAs.

ShareIt link:

Get more info