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Life Cycle Assessment: Principles, Practice and Prospects
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Chapter 5, agriculture accounts for 62% of land use and some two-thirds of Australia’s water
use. So, how may life cycle assessment (LCA) assist in assessing impacts and driving the uptake
of more sustainable techniques in agriculture?
In this Chapter, case studies are used to illustrate some of the key developments and appli-
cations of LCA to agriculture in Australia. The initial challenges for application of LCA to
agriculture are introduced in Section 9.2. In Section 9.3, sugar cane production in Queensland
is used to explore optimal land uses and functional aspects of agricultural production within a
socioeconomic context. In Section 9.4, an example from dairy farming is used to illustrate the
importance of taking a ‘whole-of-system’ view of water use in milk production and consump-
tion. In Section 9.5, the balance between agricultural production and food processing is again
explored, this time through an example of maize production and corn products. In Section
9.6, examples from ‘food miles’ and other ‘farm-fresh produce’ debates are reviewed in the
context of LCA in agriculture. In Section 9.7, discussion focuses on the methodological and
research challenges in application of LCA for policy and decision-making in agriculture, while
Section 9.8 concludes with a brief consideration of the future potential utilisation of LCA in
the context of farming in an environmentally constrained world.

9.2 Issues in the application of LCA to agricultural systems
There is generally a lower level of maturity in the methods and techniques of LCA regarding
the assessment of agricultural systems compared to industrial systems. The setting of system
boundaries is often more complex, as agricultural systems are relatively open, involving land,
biodiversity, and a range of interrelated hydro-bio-chemical processes. The development of
reliable datasets in LCA generally relate back to its origins in energy assessment and analysis of
manufacturing processes. Hence, data relating to biodiversity change, soil-crop dynamics and
other aspects of agricultural systems are generally less available in LCA inventories than energy
data required for industrial processes.
Although agricultural LCA data is becoming more widely available, data shortages continue
to challenge the widespread use of LCA for agricultural practices. Moreover, the diversity of
these practices and the variables involved are significant. Even for a single agricultural product,
there is typically a range of different types or modes of production. For example, meat produc-
tion may involve different species farmed at different intensities using varying amounts of land
and water, with different patterns of inside/outside husbandry, in widely differing climates
and with widely differing technologies, types and amounts of inputs (food, fertiliser, infra-
structure and fuel) and outputs in the form of products and by-products (e.g. prime cuts, other
cuts, mechanically recovered fractions, tallow, bone products, hide products and offal). Of
course different system characteristics and components would apply in the case of game meat.
Similarly, there are wide ranges in variables for different dairy systems and crop systems.
Even where a single system type can be identified and data secured, significant variations
in actual LCA results may be achieved within conceivably similar parameters, across different
situations. Industrial systems for producing a particular product from a particular material
may typically involve a narrowly predictable range of operating conditions within a well-
defined (indoor) system. However, in agriculture, within a single field, soil moisture variations
may determine widely different soil greenhouse emissions, or different tenants on neighbour-
ing farms may adopt varying practices in fertiliser or pesticide application from year to year. In
other words, LCA is most readily applied to predictable, replicable, closed systems, which agri-
culture is not.
Nevertheless, LCA has already made contributions to the environmental assessment of
agricultural systems. Notably, it has been used to identify so-called ‘counter-intuitive’ results,

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