Frequently Asked Questions on Data Sources


What are the data sources used by GAPI?

What are “nei” species?

How do you calculate proximity to target?

What is winsorisation?

How is the final Species-Country Pair score calculated?

How are species and country scores calculated?

How do normalised and cumulative scores differ from each other?

Why are green house gas emissions or human health concerns not addressed (i.e. chemical residue in tissues)?


What are the data sources used by GAPI?

Data sources include FAO production data, national and regional statistics, seafood industry trade press and figures, scientific literature, etc. To see a complete list of referenced sources for each Indicator, click here.

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What are “nei” species?

FAO does not always report 2007 production at the species level and instead reports production of an aggregate group of species. In these cases FAO lists the group as “nei” or “not elsewhere included”. To determine the actual species of production within these “nei” groups, we cross-referenced each of the “nei” groups that appears among the top 20 species with information on production in that specific country. For instance, FAO reports 41,900 mT of “seabass nei” produced in Turkey. However, assessment of the Turkish aquaculture industry suggests that Turkey’s production is dominated by or is entirely European seabass. Thus, GAPI assumes that the 41,900 mT of “seabass nei” are actually European seabass. The top 20 marine finfish aquaculture species included five “nei” groups: amberjacks nei, lefteye flounder nei, seabass nei, and porgies/seabreams nei. GAPI assessed these as Japanese amberjack, bastard halibut, European seabass, and red seabream, respectively. Since all of these species appeared elsewhere in the top 20 list, GAPI simply added the production values of each of these species to its counterparts in the top 20 list. Thus, no new species were added to the top 20 list.

GAPI treats one group, “groupers nei”, somewhat differently from the rest. FAO data indicate that “groupers nei” was one of the top 20 species or species groups in production in 2007. However, the proportion of production that each of these species comprises is unclear. Since ecological performance is similar across farmed grouper species, some generalizations can be made. Thus, “groupers” was maintained as a generic group where performance is assumed to be consistent across individual grouper species.

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How do you calculate proximity to target?

To be able to compare performance among escaping fish and the sustainability of feed sources, for example, it is necessary to standardize all data on the same 0-to-100 scale, where individual scores can be compared in a statistically meaningful way. The proximity-to-target approach was used to calculate how close each performer is to meeting the established precautionary targets. Proximity-to-target is calculated for each individual indicator separately, using the following formula:

Proximity-to-target = 100 – [100 x (Actual Performance - Target)
                                                (Maximum Winsorised Value - Target)]

This results in an initial, unweighted score for each of the ten indicators. The worst performer in the pool sets the floor for performance (is assigned a score = 0). Thus scores of the balance of players is partially dependent on the pool of players included in the analysis. For this reason it is essential the pool of performers is representative of the entire sector of the marine finfish aquaculture industry. The performers included within GAPI comprise approximately 94% of marine finfish aquaculture by production and 91% by value, which is a solid representation of the global marine finfish aquaculture industry.

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What is winsorisation?

GAPI assumes that performance by a player that sits far outside (greater than two standard deviations) the range of performances of its cohort is a reporting artifact, not an accurate reflection of performance.  Winsorisation is a common statistical approach to dealing with such outliers. It addresses the infrequent appearance of extremely high or low values in a data set so that those values do not distort the distribution of the entire data set. In winsorisation, if any performance lies outside of the normal distribution of data for the entire group of performers, that outlier performance value is adjusted so that it lies at the extreme edge of the normal range (in other words, the value is adjusted to a value two standard deviations from the norm). Since the target performance is set at zero, however, no performer can over perform (i.e., do better than zero impact). Thus, winsorisation is only used to adjust for extreme underperformance (i.e., performing significantly worse in any one indicator than the data set would suggest is plausible).

Winsorisation
Winsorisation

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How is the final Species-Country pair score calculated?

The standard unit of analysis within GAPI is the species-country pair. For each of the 20 farmed species and 22 producing countries assessed within GAPI, there is a corresponding GAPI score. This results in GAPI scores for 44 species-country pairs such as Atlantic salmon–Norway, Atlantic cod–Iceland, and barramundi–Australia. Step 8 of deriving the GAPI score demonstrates how the final GAPI score for each species-country pair is calculated for a hypothetical performer.

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How are species and country scores calculated?

While GAPI assesses performance at the species-country level, it is critical that these individual GAPI scores can be aggregated so that conclusions can be made about performance across farmed marine finfish species and the countries in which they are produced. While one user might be interested in how Atlantic salmon–Chile scores, another user may be more interested in how Chile’s marine finfish industry is performing overall or how Atlantic salmon scores vary across producing countries or how Atlantic salmon compare to Atlantic cod in general. Step 9 of deriving the GAPI scores demonstrates how scores are aggregated.

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How do normalised and cumulative scores differ from each other?

One of the major contributions of GAPI is the ability to compare normalised and cumulative scores. Normalised scores measure the intensity of environmental impacts per unit of production. In essence, these score the “efficiency” of production systems. These scores can assist policy makers in developing regulations that can improve the relative performance of their aquaculture industry. They provide an apples-to-apples comparison against other industries or countries, regardless of their size.

In contrast, cumulative scores look at the overall impact of aquaculture production. Cumulative scores encourage policy makers to grapple with important questions of industry scale and carrying capacity. Both measures are important. To pull an analogy from climate change, CO2 emissions have a minor impact on a normalised basis compared to methane (a ton of methane is orders of magnitude more damaging than a ton of CO2). However, the cumulative global emission of CO2 results in an earth-changing impact. As a consequence, governments are attempting to address emissions of both gases. Likewise normalized and cumulative impacts of aquaculture must be addressed simultaneously.

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Why are green house gas emissions or human health concerns not addressed (i.e. chemical residue in tissues)?

GAPI currently focuses only on the immediate ecological impact of aquaculture. While human health, social or economic issues are certainly important and  interwoven with ecological performance and deserving of equal scrutiny, for the time being GAPI considers only explicitly environmental impacts of aquaculture that can be traced directly to on the water performance of production systems. 

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