## 3 Results
### Composite Indicator Approach
#### 3.2.5 Aggregation Methods  
The arithmetic mean (Equation (3)) and the addition of sub-indicators (Equation (4)) are the most common aggregation approaches in the analyzed studies.  
\[\text{Overall Index Score}= \frac{\sum_{i=1}^{n}Xi}{n}\times 100 \tag{3}\] \[\text{Overall Index Score}= \sum_{i=1}^{n}Xi \tag{4}\]  
where \(n\) is the number of sub-indicators, and \(X\) is the score of the sub-indicator \(i\).  
The arithmetic mean aggregation method has a simple calculation, but it implicitly assumes the substitutability of sub-indicators, since lower levels of a given sub-indicator can be compensated by higher levels of a different one, being unable to reveal the most representative values of the situation under study. The consideration of only the aggregated scores can lead to inappropriate decisions on which priority actions should be implemented to enhance biodiversity performance [7]. This limitation can be partially minimized by allowing the user to visualize the scores in each aggregation level [7].  
The additive approach with equal weights also assumes that the scores for each indicator are substitutable. [PERSON] et al. [40] followed this approach, and, to measure each indicator, the authors formulated questions for experts. The answers to all the individual questions were translated into six risk levels (from one, no risk, to six, very high risk). The total sums for each question were equally weighted to obtain a single biodiversity impact score. However, the authors reported that the obtained index had little physical meaning. In such cases, it is necessary to guide the results' interpretation to minimize difficulties. [PERSON] et al. [40] used scores for historic or natural states to give meaning to the proposed indicator. If the BioImpact scores were greater, that would represent a more negative biodiversity impact, and an equal/lower value would represent a more positive biodiversity impact.  
[PERSON] et al. [34] used \"if-then\" linguistic rules to determine the classification of the aggregated indicator \(Y\). Considering that it is composed of two indicators, X1 and X2, with a low and high qualitative classification, respectively, it can be defined as \"if X1 is low and X2 high, then Y is high\". The use of linguistic rules simplifies the understanding of the results. On the one hand, qualitative models are easier to understand. On the other hand, this method lacks sensitivity due to its qualitative form and the uneven frequency distribution on the side of the \"low class\". To mitigate this issue, the authors proposed the design of a quantitative indicator based on fuzzy decisions [34].  
[PERSON], [PERSON], and [PERSON] [30] used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the generalized distance measure (GDM) to assess farm biodiversity according to the farmer's perspective. The elaboration of the synthetic indicator using the TOPSIS method requires the calculation of the distance of each object (farm) from the adopted best and worst condition, obtained through the GDM. According to the index values, the farms were classified into four classes, namely, green farms (high nature value), yellow farms (medium-to-high nature value), grey farms (medium-to-low nature value), and black farms (low nature value). This classification permitted the use of the generalized logit model of ordered categories to identify the socio-economic characteristics associated with a higher assessment of the natural attractiveness of farms, such as the gender and age of the farmer, and the size of the farm [30]. The study of [PERSON], [PERSON], and [PERSON] [30] also included farmers' opinions about the natural value of their farms, specifically regarding the importance of pollinators, the conditionings of game species, and the implications of the species richness of bird, amphibian, reptile, and rodent populations, as well as the species richness of non-crop (wild) plants.