SAMPLING PROCESSES FOR Carapa guianensis Aubl. IN THE AMAZON: SAMPLING PROCESSES FOR ANDIROBA

• Autor(a) principal: Vieira, Diego dos Santos
• Data de Publicação: 2018
• Outros Autores: Oliveira, Marcio Leles Romarco de, Gama, João Ricardo Vasconcellos, Oliveira, Bruno Lafetá, Rego, Anna Karyne Costa, Bezerra, Talita Godinho
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Cerne (Online)
• Texto Completo: https://cerne.ufla.br/site/index.php/CERNE/article/view/1828
• topic: Adaptive cluster sampling; Simple random sampling; Systematic samplingn; SAMPLING PROCESSES; ECOLOGY; Adaptive cluster sampling; Simple random sampling; Systematic samplingn; PROCESSOS DE AMOSTRAGEM; ECOLOGIA

The objective of this study was to analyze the adaptive cluster sampling (ACS), simple random sampling (SRS) and systematic sampling (SS) processes to obtain the number of ha-1 trees of Carapa guianensis Aubl. in the Amazon. The data were obtained through 100% inventory and sampling simulations, considering a DBH ≥ 25 cm, a sampling intensity of 4%, a maximum error of 10% and plots of 0.09, 0.16 and 0.25 ha. The last two sizes were only used to analyze their effect on the ACS estimators. The processes were evaluated for accuracy, precision (E%) and confidence interval (CI), while the mean ha-1 of the processes were compared with that of the 100% inventory by the Z test. The ACS process showed no significant difference between its average ha-1 trees and the 100% inventory, and it was also the most accurate and the only one whose CI was true. However, it presented a final sample intensity 3.6 times greater than the simple and systematic random samplings, in addition to E% above 10%, which makes it unacceptable, legally, and economically unfeasible. The other processes had densities significantly higher than the 100% inventory, with sample intensities lower than ACS and E% lower than 10%, making them legally viable. The use of larger plots in the ACS implies larger clusters and a greater tendency to underestimate the number of trees, resulting in larger sample errors and less accuracy.