Multilevel environmental assessment of regional farming activities with Life Cycle Assessment: Tackling data scarcity and farm diversity with Life Cycle Inventories based on Agrarian System Diagnosis,Agricultural Systems

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Multilevel environmental assessment of regional farming activities with Life Cycle Assessment: Tackling data scarcity and farm diversity with Life Cycle Inventories based on Agrarian System Diagnosis
Agricultural Systems ( IF 6.1 ) Pub Date : 2021-12-07 , DOI: 10.1016/j.agsy.2021.103328
L. Pradeleix ₁ , P. Roux ₂ , S. Bouarfa ₁ , V. Bellon-Maurel ₂

Affiliation

CONTEXT

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.

OBJECTIVE

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.

METHODS

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.

RESULTS AND CONCLUSIONS

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.

SIGNIFICANCE

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.

中文翻译：

使用生命周期评估对区域农业活动进行多层次环境评估：使用基于农业系统诊断的生命周期清单解决数据稀缺和农场多样性问题

语境

政策制定者仍然缺乏在区域范围内评估农业对环境影响的方法。生命周期评估 (LCA) 以评估经济活动的环境足迹而闻名；但是，必须对其进行调整才能用于此目的。

客观的

我们的目标是开发一种在区域范围内进行相关农业生产 LCA 的方法，特别是解决 LCA 不确定性来源的两大挑战，即数据稀缺性和农业系统多样性。

方法

我们引入了一种建立农业区域生命周期清单 (LCI) 的创新方法，能够在数据稀缺的情况下捕捉农业系统的多样性。它将 LCA 与 Agrarian System Diagnosis (ASD) 相结合，后者已经过调整以满足 LCI 步骤的大量数据要求。这种我们称为“基于 ASD 的 LCI”的方法被应用于突尼斯凯鲁万半干旱灌溉平原。

执行 ASD 后，使用不同的数据源构建耕作系统类型：文献综述和基于 ASD 的数据（例如，历史和景观分析、访谈）。这为对农场进行分层抽样铺平了道路，然后通过实地访问和广泛访谈对每个选定的农场进行深入研究，以收集活动数据，即与能源和材料输入和输出流（例如，粪便）相关的数据。、种子、电力）在作物/牲畜层面。通过使用各种方法（例如类比、作物建模或专家知识）填补剩余的数据空白来提高数据集质量。