Multi-Criteria Analysis
Multi-criteria analysis (MCA) is a class of procedures designed to aid decision-making in a structured manner where multiple conflicting objectives are involved. Such procedures are very useful in multifunctional disciplines such as agriculture, where all management choices have multiple effects and involve trade-offs within and between the social, economic, and environmental spheres. There are a great number of different MCA procedures but all are based on assigning weights to different criteria in order to arrive at an integrated evaluation of options. 
What is Multi-Criteria Analysis?  

Multi-criteria problems are common occurrences. For example, a farmer’s decision over which crops to plant may depend on factors such as expected revenues, risk of crop failure, time requirements or personal preferences, which need to be weighed against each other in order to find an optimal solution. However, direct comparison is often impossible because of the different dimension in which criteria are assessed (e.g., how to weigh a higher average profit against a higher risk of crop failure)?

 

Usually, such decisions are made intuitively based on previous experience. However, certain settings such as government, science or business optimisation require a more structured approach, either in the interest of transparency and accountability, or because problems are too complex to solve intuitively.

 

MCA is a term used for procedures that evaluate data from multiple categories in order to arrive at an integral score. It provides a tool for dealing with the inevitable trade-offs within complex decision-making situations that may also feature high uncertainty, different forms of information, and multiple stakeholder interests and perspectives. For this reason, MCA has gained momentum as a methodology for the evaluation of sustainability (Adams & Ghaly, 2007).

 
MCA procedures are very useful with regards to multifunctional disciplines such as agriculture, where management choices have multiple effects and involve trade-offs within and between the social, economic, and environmental spheres. MCA facilitates comparison of very different criteria and the weighing of their importance in order to make an evaluation of trade-offs possible.  

In order to better understand the practicalities of MCA, it is useful to consider a simplified example where a farmer needs to choose between three alternative fertilisation scenarios for a wheat field and is concerned with two impact areas: provisioning service (crop yield) and nitrogen use efficiency (NUE). 

 

a) Scenario definition
Scenario A is a minimal fertiliser application scenario which results in a low yield but a high nitrogen use efficiency (NUE). Scenario B is an intensive scenario with high fertiliser application but low NUE. Scenario C represents an intermediate approach between the two. The values shown represent the expected results if the scenario were adopted.

 
Scenario Provisioning t/ha Nitrogen Use Efficiency
A 3 65%
B 8 40%
C 6 55%

 

b) Converting all values into dimensionless scores
The next step of the MCA is to convert these values onto a numerical scale, which allows direct comparison between values. One common approach is to allot scores between 0 and 100, with 100 representing the best possible result within the respective category and 0 representing the worst (Dodgson et al., 2009).

 

It is crucial to the validity of the MCA result that scores are determined as objectively as possible. In the current example for the provisioning column, it is assumed that the maximum expected yield is 10 t/ha and the minimum is 2 t/ha. Therefore a maximum score of 100 is given if 10 t/ha (or more) are obtained and a minimum score of 0 is given if 2 t/ha (or less) are obtained. Intermediate values are given proportionate scores along the scale.

 

The scores for NUE are calculated in the same way albeit with different maximum and minimum values. The scores below reflect a maximum NUE of 80% or above (100 points) and a minimum NUE of 10% or less (0 points).

 

 
Scenario Provisioning t/ha Nitrogen Use Efficiency
A 3 65%
B 8 40%
C 6 55%

 

 

c) Assigning weighs to each impact category and multiplying with scores

Once values have been converted onto a comparable numerical scale, the next stage is to assign weights to the impact areas that represent their relative importance. 

 

For example, the farmer could decide to value both criteria equally:

provisioning service (crop yield):                        50 %

nitrogen use efficiency (NUE):                            50 %

 

or they could decide to put stronger emphasis on the nitrogen use efficiency:

provisioning service (crop yield):                        30 %

nitrogen use efficiency (NUE):                            70 %

 

 

 

d) Calculating integrated scores and final assessment
If provisioning service and NUE are considered by the farmer to have equal importance (50% each), then the end result is obtained simply by calculating the average score for each scenario. In this case, scenario B would be the most desirable outcome:

 

 
Scenario Provisioning t/ha Nitrogen Use Efficiency
A 3 65%
B 8 40%
C 6 55%
C 6 55%
However, it is more likely that different weights are assigned to different impact areas. Assuming that a weighting is given of 30% to provisioning service and 
70% to NUE, the best outcome is expected from scenario C.
 
Scenario Provisioning t/ha Nitrogen Use Efficiency
A 3 65%
B 8 40%
C 6 55%
C 6 55%
 
Obviously, the end evaluation result is strongly influenced by the weights assigned to the different criteria. The weighing process is inherently subjective and therefore it is important to clearly justify weighing decisions in the interest of transparency. Decision-makers may rely purely on their own preferences during this process or choose to include the views of multiple stakeholders. 

Adams, M. A., & Ghaly, A. E. (2007). The foundations of a multi-criteria evaluation methodology for assessing sustainability

 

The International Journal of Sustainable Development & World Ecology, 14(5), 437-449

 

Dodgson, J. S., Spackman, M., Pearman, A., & Phillips, L. D. (2009). Multi-criteria analysis: a manual.

 
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