Policymakers still lack methods to assess the environmental impacts of agriculture at regional scales. Life Cycle Assessment (LCA) is renowned in assessing the environmental footprint of economic activities; however, it has to be adapted to be of use for this purpose.
Our objective is to develop a methodology to carry out relevant LCA of agricultural productions at the regional scale, and to address, in particular, two major challenges which are sources of uncertainties in LCA, i.e., data scarcity and farming system diversity.
We introduce an innovative method for building Life Cycle Inventories (LCI) of agricultural regions, capable of capturing farming system diversity in the context of data scarcity. It combines LCA with Agrarian System Diagnosis (ASD), which has been adapted to meet the heavy data requirements of the LCI step. This method, which we named “ASD-based LCI”, was applied to the semi-arid irrigated plain of Kairouan in Tunisia.
After ASD is carried out, a typology of farming systems is built using different data sources: literature review and ASD-based data (e.g., historical and landscape analysis, interviews). This paves the way for a stratified sampling of farms, after which each selected farm is studied in-depth, through field visits and extensive interviews, to collect activity data, i.e., data related to energy and material input and output flows (e.g., manure, seeds, electricity) at the crop / livestock level. The data set quality is improved by filling remaining data gaps using various approaches, e.g., analogy, crop modelling, or expert knowledge. The effect of stratified sampling and data gap filling on uncertainty reduction is evaluated using the pedigree matrix approach and the “uncertainty factor” (UF) which determines the uncertainty interval around the mean of any LCI data.
Nine farming systems - including three “corporate agriculture”, five “family agriculture” and one “landless farmer” archetypes - and seventy cropping and livestock systems were characterized. The pedigree matrix approach showed that - with regards to statistics-based data- the uncertainty interval could be reduced twofold, and by a multiple of four with ASD-based LCI, without or with extrapolation, respectively.
Not only has the ASD-based LCI method proven powerful when building LCI in agriculture at the regional scale with reduced uncertainty, but it is also suited to the quantification of material flow exchanges within the farm and across farms, which is of valuable service when assessing agroecological productions, which promotes circularity.