Document Type


Degree Name

Doctor of Philosophy (PhD)


Geography & Environmental Studies

Program Name/Specialization

Environmental Resource Management


Faculty of Arts

First Advisor

Dr. Alison Blay-Palmer, Associate Professor

Advisor Role

Academic Advisor


Multi Attribute Utility Theory (MAUT), Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and Elimination methods of Multi-Criteria Decision analysis (MCDA) are tested to assess and compare the sustainability of different agricultural systems. Indicators and composite indicators are derived from data gathered using the agricultural sustainability categories of Productivity, Stability, Efficiency, Durability, Compatibility and Equity (PSEDCE).

Agricultural systems around the world face challenges from current agricultural practices, over-exploitation of natural resources, population growth and climate change. As a result, understanding agricultural sustainability has become a global issue. Assessment is a first step in benchmarking and tracking agricultural sustainability and can support related policy and programmes. This thesis applied the PSEDCE categories to understand more about the complexities inherent to agricultural sustainability assessment.

Agricultural sustainability assessment (ASA) requires a wide variety of ecological, economic and social information with various methods. In the first part of this thesis, a systematic analysis of the scientific soundness and user-friendliness of eight ASA approaches revealed that MCDA based ASA is the preferred holistic method. MCDA can take into account both qualitative and quantitative indicators of all dimensions of sustainability and analyze them to draw a comprehensive picture. As a multifaceted, complex issue, agricultural sustainability assessment is well-suited to MCDA, which is able to handle large data sets including stakeholders’ perspectives. Given that it is a relatively new analysis procedure in the study of agriculture, only a few researchers have applied this technique to measure sustainability. Considering these findings, three MCDA methods, MAUT, PROMETHEE and Elimination, were tested to measure the relative sustainability of five agricultural systems in coastal Bangladesh.

To investigate the performance of MAUT, PROMETHEE, and Elimination, a total of 50 indicators from agricultural sustainability categories of PSEDCE were tested. From these 50 indicators, 15 composite indicators were developed through proportionate normalization and hybrid aggregation rules of arithmetic mean and geometric mean. The 15 composite indicators were used in MAUT and PROMETHEE analysis, and the 50 indicators were used in Elimination analysis.

The analyses show that MAUT is able to aggregate diverse information and stakeholders’ perspectives to generate a robust score that enables a comparison of sustainability across the different agricultural systems. PROMETHEE is a non-compensatory approach that can also accommodate a variety of information and provide thresholds for ranking relative agricultural sustainability for each of the five agricultural systems. Elimination ranks the sustainability of agricultural systems through a set of straightforward decision rules expressed in the form of “if … then …” conditions. Elimination appears to be quick and less complex, whereas MAUT and PROMETHEE are regarded as fairly complicated and require software to find potential solutions.

Overall, the study shows that MAUT, PROMETHEE and Elimination can handle multidimensional data and can be applied for relative assessment of sustainability of agricultural systems. However, selection of the appropriate criteria, stakeholders’ perspectives and the purpose of the assessment are very important and must be considered carefully for inclusion in MCDA methods for agricultural sustainability assessment. The results of the case studies also demonstrate that these approaches have the potential to become a useful framework for agricultural sustainability assessment and related policy development and decision-making.

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