Modeling Cropland Footprint using LCA

Cropland footprint modelling was undertaken using LCA. Initially, cropland footprint data for Australian agricultural commodities were obtained from a previous study [31 (link)]. This included Australian-produced livestock products, taking into account the cropland footprints associated with feed rations. Australia is a net exporter of most agricultural commodities and it has been estimated that greater than 90% of available food is Australian-grown [58 (link),59 ]. This data source [31 (link)] also describes the cropland footprints of the main agricultural commodities that are imported because they are not grown in Australia to any significant extent, namely coffee bean, cocoa bean, tea, coconut, hazelnut, hops and oil palm fruit. In brief, these data were developed using a 1.1 km² spatial resolution map of agricultural production including crop yield information and involved the application of three life cycle impact assessment models, described below. Detail is available in the associated reference [31 (link)]. The cropland footprints of individual foods were quantified using conversion factors that translate agricultural commodities into retail products and edible portions, as described previously [49 (link)]. Former cropland areas occupied by food-processing factories, transportation systems and other infrastructure were deemed not materially important and were excluded from the assessment.
Three cropland footprint indicators were quantified for each individual adult daily diet, reflecting different environmental concerns relating to cropland occupation. Firstly, a cropland scarcity footprint (CSF) was quantified. Cropland is a globally finite and scarce natural resource and the occupation of cropland contributes to this scarcity as cropland used for one productive purpose cannot be used for another. However, not all cropland is equally productive. Therefore, CSFs were quantified taking into account productive capability using the net primary productivity of potential biomass at each location, reflecting the natural capability of the land. CSF results were expressed in m² yr-e (equivalent), with cropland of global average productivity as the reference. Secondly, a cropland biodiversity footprint (CBF) was quantified using the biodiversity impact factors of Chaudhary and Brookes [60 (link)]. These impact factors report potential species loss based on 5 taxa in 804 ecoregions of the world. CBF results were expressed as potentially disappeared fraction of species (PDF). Thirdly, a cropland malnutrition footprint (CMF) was quantified using the impact factors of Ridoutt et al. [26 (link)]. These impact factors report potential protein-energy malnutrition impacts considering potential domestic and trade-related food deficits arising from cropland occupation. The factors are highest in countries where protein-energy malnutrition is prevalent and in countries that share a trade relationship with these regions. As is typical in LCA, the factors express the potential impact of production (i.e., occupying the cropland) and not the potential benefits of use (i.e., food consumption). Crop products have the potential to be used in numerous ways: for direct human consumption, for livestock rations, for biofuels or other industrial products. Even when crop products are intended for human consumption, they may be wasted or contribute to energy intakes that exceed a healthy diet. CMF results were expressed in disability-adjusted life years (DALYs). To calculate cropland footprint results, each spatially explicit instance of cropland occupation was multiplied by the spatially relevant impact factor. Cropland footprint results for almost 150 separate food items are presented in the Supplementary Materials, Table S3.