IIProductionUnknown:Analyzing Data Through of Percentage of Importance Indice
(Production Unknown) and Its Derivations
The Importance Index (I.I.) can determine the loss and solution sources for a system in certain knowledge areas (e.g.,
agronomy), when production (e.g., fruits) is known
(Demolin-Leite, 2021). Events (e.g., agricultural pest) can
have different magnitudes (numerical measurements),
frequencies, and distributions (aggregate, random, or regular)
of event occurrence, and I.I. bases in this triplet
(Demolin-Leite, 2021)
<https://cjascience.com/index.php/CJAS/article/view/1009/1319>.
Usually, the higher the magnitude and frequency of aggregated
distribution, the greater the problem or the solution (e.g.,
natural enemies versus pests) for the system (Demolin-Leite,
2021). However, the final production of the system is not
always known or is difficult to determine (e.g., degraded area
recovery). A derivation of the I.I. is the percentage of
Importance Index-Production Unknown (% I.I.-PU) that can detect
the loss or solution sources, when production is unknown for
the system (Demolin-Leite, 2024)
<DOI:10.1590/1519-6984.253218>.
