An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies

Abstract

Eco-efficiency is a useful guide to dairy farm sustainability analysis aimed at increasing output (physical or value added) and minimizing environmental impacts (EIs). Widely used partial eco-efficiency ratios (EIs per some functional unit, e.g. kg milk) can be problematic because (i) substitution possibilities between EIs are ignored, (ii) multiple ratios can complicate decision making and (iii) EIs are not usually associated with just the functional unit in the ratio's denominator. The objective of this study was to demonstrate a 'global' eco-efficiency modelling framework dealing with issues (i) to (iii) by combining Life Cycle Analysis (LCA) data and the multiple-input, multiple-output production efficiency method Data Envelopment Analysis (DEA). With DEA each dairy farm's outputs and LCA-derived EIs are aggregated into a single, relative, bounded, dimensionless eco-efficiency score, thus overcoming issues (i) to (iii). A novelty of this study is that a model providing a number of additional desirable properties was employed, known as the Range Adjusted Measure (RAM) of inefficiency. These properties altogether make RAM advantageous over other DEA models and are as follows. First, RAM is able to simultaneously minimize EIs and maximize outputs. Second, it indicates which EIs and/or outputs contribute the most to a farm's eco-inefficiency. Third it can be used to rank farms in terms of eco-efficiency scores. Thus, non-parametric rank tests can be employed to test for significant differences in terms of eco-efficiency score ranks between different farm groups. An additional DEA methodology was employed to 'correct' the farms' eco-efficiency scores for inefficiencies attributed to managerial factors. By removing managerial inefficiencies it was possible to detect differences in eco-efficiency between farms solely attributed to uncontrollable factors such as region. Such analysis is lacking in previous dairy studies combining LCA with DEA. RAM and the 'corrective' methodology were demonstrated with LCA data from French specialized dairy farms grouped by region (West France, Continental France) and feeding strategy (regardless of region). Mean eco-efficiency score ranks were significantly higher for farms with <10% and 10% to 30% maize than farms with >30% maize in the total forage area before correcting for managerial inefficiencies. Mean eco-efficiency score ranks were higher for West than Continental farms, but significantly higher only after correcting for managerial inefficiencies. These results helped identify the eco-efficiency potential of each region and feeding strategy and could therefore aid advisors and policy makers at farm or region/sector level. The proposed framework helped better measure and understand (dairy) farm eco-efficiency, both within and between different farm groups.

The Relationship of Dairy Farm Eco-Efficiency with Intensification and Self-Sufficiency. Evidence from the French Dairy Sector Using Life Cycle Analysis, Data Envelopment Analysis and Partial Least Squares Structural Equation Modelling

“…This study builds on the dairy farm exercise by Soteriades et al [10] for French specialized dairy farms (see above) which combined LCA with DEA to calculate dairy farm eco-efficiency. A brief description of their exercise (data, model and results) is therefore given below before turning to the PLS-SEM framework employed here to study the relationships of eco-efficiency with intensification and self-sufficiency.…”

“…Numerous studies use LCA to assess dairy farm environmental performance by calculating ‘eco-efficiency’ ratios, that is, environmental impacts expressed per unit of milk or land area [5,6,8,9]. Yet eco-efficiency ratios have several drawbacks [10], for example the allocation of environmental impacts to several dairy farm products (milk, meat, crops) is challenging. Dairy studies are therefore increasingly coupling LCA indicators with the multiple-input, multiple-output method Data Envelopment Analysis (DEA [11]) to calculate single aggregated eco-efficiency scores per farm, by accounting for all LCA impacts (or carbon foot-printing indicators), inputs and outputs simultaneously [10,12–19].…”

“…The global efficiency indicator is based on a ratio between productions of goods and inputs (or environmental impacts). A wealth of dairy studies [5,6,8,10,13,19–22] show that eco-efficiency is generally influenced by two management strategies improving this ratio with an increase in outputs or a decrease in inputs: (i) the level of intensification at animal and farm-levels, i.e. higher production per unit of input so that increases in outputs outweigh potential increases in inputs and environmental impacts; and (ii) the farm’s degree of self-sufficiency, i.e.…”

“…By coupling LCA data with DEA, Soteriades et al [10] found that the less the proportion of maize silage in the total forage area of French specialized dairy farms, the higher the farms’ eco-efficiency. This confirmed earlier findings that increasing maize silage in dairy farms generally implies higher animal and farm-level farming intensity and lower farm self-sufficiency (higher supplementation with protein-rich feeds, usually imported soybean meal) [5,8,26,27].…”

“…The objective of this paper was to explore the relationships between dairy farm eco-efficiency, self-sufficiency, and farm and animal-level intensification, by extending the LCA-based dairy farm exercise of Soteriades et al [10] that accounted for impacts both on and off-farm. That way it was possible to determine whether or not on-farm management practices promoting self-sufficiency and/or increasing input per ha and output per unit input- especially per ha- could explain dairy farm environmental performance at a larger scale.…”