SEQUENTIAL SAMPLING FOR YIELDS MONITORING IN KINGWORM (ZOPHOBAS SPP. [COLEOPTERA: TENEBRIONIDAE]) FARMS IN THE AMAZON
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| 1. | Title | Title of document | SEQUENTIAL SAMPLING FOR YIELDS MONITORING IN KINGWORM (ZOPHOBAS SPP. [COLEOPTERA: TENEBRIONIDAE]) FARMS IN THE AMAZON |
| 2. | Creator | Author's name, affiliation, country | Carlos Daniel Vecco - Giove; Universidad Nacional de San Martín; Peru |
| 2. | Creator | Author's name, affiliation, country | Hitler Panduro Salas; Estudios Amazónicos; Peru |
| 2. | Creator | Author's name, affiliation, country | Milton Francisco Ubeda-Olivas; Estudios Amazonicos; Nicaragua |
| 2. | Creator | Author's name, affiliation, country | Basilia Miriam Fernández Argudín; Estudios Amazónicos; Cuba |
| 2. | Creator | Author's name, affiliation, country | Ileana . Miranda Cabrera; Centro Nacional de Sanidad Agropecuaria, La Habana, Cuba; Cuba |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | spatial pattern, Taylor's Power Law, size sample. |
| 4. | Description | Abstract | Background: Sampling for monitoring commercial production of kingworm (Zophobas spp.) requires knowledge about optimal sample sizes and methods for projecting yields. Hypothesis and objective: A sequential enumerative sampling (SES) based on larval weight records should present significant levels of productivity prediction under known margins of error and precision. To verify this, it was proposed to evaluate the performance of an SES method in two kingworm’s farms in Tarapoto, Peruvian Amazon. Methodology: Means (m) and respective variances (s2) of larvae number and weight since six sample units (SU) of 500 ml from 35 production units (PU) of 48 l were obtained. Log-transformed data were fitted to Taylor’s Power Law TPL (log s2 =log a +b. log m). A Morisita’s index transformation was applied with original larvae number data to obtain an independent measure of intra-sample spatial arrangement. TPL elements constituted the Green's function optimal sample size for three margins of error E. Sequential sampling simulations were carried out, whose means predicted yields comparing two methods of weight relationship between SU and PU (w/W, linear function), with their respective census. Results: Number of larvae (s2 =1.06 m1.61) and weights (s2 =0.79 m1.53) showed highly significant adjustments in TPL, with aggregation coefficients corresponding crowded spatial arrangement. Larval density and Ip index showed close correlation. Although simulations provided a lower hit frequency than expected, the mean precision increased highly significantly while increasing the error margin, with detection levels of 0.25, 2.23 and 12.57 g to E 50, 30 and 20 %, respectively. Implications: Applying SES, kingworm’s breeders should standardize substrate volume contained in each UP and adjust their w/W conversion factor to avoid plus sampling efforts. Conclusion: The SES suits the needs of productivity monitoring, where using of w/W ratio and 50 % margins of error are associated with greater effectiveness and precision with a sample size of ni<3. |
| 5. | Publisher | Organizing agency, location | Universidad Autónoma de Yucatan |
| 6. | Contributor | Sponsor(s) | Programa Nacional de Investigación en Pesca y Acuicultura |
| 7. | Date | (YYYY-MM-DD) | 2023-01-05 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://www.revista.ccba.uady.mx/ojs/index.php/TSA/article/view/4442 |
| 10. | Identifier | Uniform Resource Name (URN) | http://www.revista.ccba.uady.mx/urn:ISSN:1870-0462-tsaes.v26i1.44426 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://dx.doi.org/10.56369/tsaes.4442 |
| 11. | Source | Title; vol., no. (year) | Tropical and Subtropical Agroecosystems; Vol 26, No 1 (2023): (January - April) |
| 12. | Language | English=en | es |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2022 Carlos Daniel Vecco Giove This work is licensed under a Creative Commons Attribution 4.0 International License. |