Proposal to obtain the optimal sample size of pests with an excess of zeros

Authors

  • Luis Gabriel Otero-Prevost Colegio de Postgraduados-Campus Veracruz. Carretera Xalapa-Veracruz km 88.5, Manlio F. Altamirano, Veracruz, México. CP. 91963
  • Juan A. Villanueva-Jiménez Colegio de Postgraduados-Campus Veracruz. Carretera Xalapa-Veracruz km 88.5, Manlio F. Altamirano, Veracruz, México. CP. 91963
  • Gustavo Ramírez-Valverde Colegio de Postgraduados-Campus Montecillo. Carretera México-Texcoco km 36.5, Montecillo, Texcoco, México. CP. 56230
  • Mónica C. Vargas-Mendoza Colegio de Postgraduados-Campus Veracruz. Carretera Xalapa-Veracruz km 88.5, Manlio F. Altamirano, Veracruz, México. CP. 91963
  • Carlos M. Becerril-Pérez Colegio de Postgraduados-Campus Veracruz. Carretera Xalapa-Veracruz km 88.5, Manlio F. Altamirano, Veracruz, México. CP. 91963
  • Lauro Soto-Rojas Colegio de Postgraduados-Campus Montecillo. Carretera México-Texcoco km 36.5, Montecillo, Texcoco, México. CP. 56230

DOI:

https://doi.org/10.29312/remexca.v15i1.3618

Keywords:

sampling, zero-inflated negative binomial, zero-inflated Poisson

Abstract

In sampling of pests with low densities, it is common to obtain a large number of zeros, which is difficult to manage since the Poisson and negative binomial probability distributions are not suitable for modeling and equations to estimate the optimal sample size are not available. In this study model the excess of zeros by estimating parameters through the methods of moments and maximum likelihood of the zero-inflated Poisson and zero-inflated negative binomial distributions, and to derive equations to calculate the optimal sample size. Systematic sampling was used to select 100 trees per grove of Río Red grapefruit (Citrus paradisi Macfad) at Finca Sayula, Veracruz, Mexico (latitude 19.20722, longitude -96.35194), from June to July 2021 and January 2022. The number of leafminers (Phyllocnistis citrella Stainton) and aphids (Toxoptera citricida Kirkaldy) present in three leaves per shoot per tree, considered as a sample unit, was counted. Simulations were performed in RStudio with different proportions of zero (0.1, 0.4, and 0.6) to compare the parameters obtained in the field using the methods of moments and maximum likelihood. Equations were derived to estimate the optimal sample size in studies of pests with low densities, based on the zero-inflated Poisson and zero-inflated negative binomial probability distributions. The method of moments yields optimal sample sizes smaller than those obtained by maximum likelihood, because they distinguish the origin from zero, so its use is recommended.

Downloads

Download data is not yet available.

References

Banik, S. and Kibria, B. M. G. 2009. On some discrete distributions and their applications with real life data. USA. JMASM. 8(2):423-447. https://doi.org/10.22237/jmasm/ 1257034020.

Cheung, Y. B. 2002. Zero inflated models for regression analysis of count data: a study of growth and development. USA. Statist. Med. 21(10):1461-1469. https://doi.org/10.1002/sim.1088.

Clay, S. A.; French, B. W. and Mathew, F. M. 2018. Pest measurement and management. In: precision agriculture basics. Shanon, D. K.; Clay, D.E. and Kitchen N. R. (eds.). Ed. ASA, CSSA, and SSSA Books. USA. 93-102 pp. https://doi.org/10.2134/ precisionagbasics.2016.0090.

Doyle, S. R. 2009. Examples of computing power for zero-inflated and over dispersed count data. USA. JMASM. 8(2):360-376. https://doi.org/10.22237/jmasm/ 1257033720.

Fang, R.; Wagner, B. D.; Harris, J. K. and Fillon, S. A. 2016. Zero inflated negative binomial mixed models: and important application to two microbial organisms important in oesophagitis. UK. Epidemiol. Infect. 144(1):2447-2455. http://doi.org/10.1017/S0950268816000662.

García-González, J. C.; López-Collado, J.; García-García, C. G.; Villanueva-Jiménez, J. A. y Nava-Tablada, M. E. 2018. Factores bióticos, abióticos y agronómicos que afectan las poblaciones de adultos de mosca pinta (Hemiptera: Cercopidae) en cultivos de caña de azúcar en Veracruz, México. México. Acta Zool. Mex. 33(3):508-517. https://doi.org/10.21829/azm.2017.3331152.

Hall, D. B. 2000. Zero inflated Poisson and binomial regression with random effects: a case study. USA. Biometrics. 56(1):1030-1039. https://doi.org/10.1111/j.0006-341x.2000.01030.x.

Hashim, L. H.; Hashim, K. H. and Shiker, M. A. K. 2021. An application comparison of two Poisson models on zero count data. UK. journal of physics: conference series, 1818(012165):1-12. http://doi:10.1088/1742-6596/1818/1/012165.

Haslett, J.; Parnel, A. C.; Hinde, J. and de Andrade, M. R., 2022. Modelling excess of zeros in count data: a new perspective on modelling approaches. USA. International statistical review. 90(2):216-236. https://doi.org/10.1111/insr.12479.

Hilbe, J. M. 2011. Negative binomial regression. Cambridge University Press. 2a Ed. UK. 346-399 pp.

Jankielsohn, A. 2017. The redesign of suitable agricultural crop ecosystems by increasing natural ecosystem services provided by insects. Hong Kong SAR China. Advances in ecological and environmental research. 1(1):365-381. http://www.ss-pub.org/wp-content/uploads/2017/09/AEER2017040501-1.pdf.

Karandinos, M. G. 1976. Optimum sample size and comments on one published formula. USA. Bull. Entomol. Soc. Amer. 22(4):417-421. https://doi.org/10.1093/ besa/22.4.417.

Lambert, D. 1992. Zero inflated Poisson regression, with an application to defects manufacturing. USA. Technometrics. 34(1):1-14. https://doi.org/10.2307/1269547.

Mullahy, J. 1986. Specification and testing of some modified count data models. Netherlands. J. Econ. 33(1):341-365. https://doi.org/10.1016/0304-4076(86) 90002-3.

Ramírez, I. C.; Barrera, C. J. y Correa, J. C. 2013. Efecto del tamaño de muestra y el número de réplicas bootstrap. Colombia. Inycompe. 15(1):93-101. https://www.redalyc.org/articulo.oa?id=291329165008.

Shannon, D. K.; Clay, D. E. and Sudduth, K. A. 2018. And introduction to precision agriculture. In: precision agriculture basics. Shanon, D. K.; Clay, D.E. and Kitchen N. R. (eds.). Ed. ASA, CSSA, and SSSA Books. USA. 1-12 pp. https://doi.org/10.2134/precisionagbasics.2016.0084.

Southwood, T. R. E. and Henderson, P. A. 2000. Ecological methods. Blackwell science. 3rd Ed. Oxford, UK. 7-66 pp. https://www.researchgate.net/publication/260051655-Ecological-Methods-3rd-edition.

Taherdoost, H. 2016. Sampling methods in research methodology, how to choose a sampling technique for research. Brazil. IJARM. 5(2):18-27. http://dx.doi.org/ 10.2139/ssrn.3205035.

Talaviya, T.; Shah, D.; Patel, N.; Yagnik, H. and Shah, M. 2020. Implementation of artificial intelligence in agriculture for optimization of irrigation and application of pesticides and herbicides. China. Artificial Intelligence in Agric. 4(1):58-73. https://doi.org/10.1016/j.aiia.2020.04.002.

Villanueva-Jiménez, J. A.; Reyes-Pérez, N. y Abato-Zárate, M. 2017. Manejo integrado de plagas y sostenibilidad. In: agricultura sostenible como base para los agronegocios. Jarquín, G. R. y Huerta, P. A. (coords.). 1a Ed. Universidad Autónoma de San Luis Potosí. México. 32-42 pp. https://www.researchgate.net/ publication/320779257-Manejo-Integrado-de-Plagas-y-Sostenibilidad.

Yesilova, A.; Kaydan, M. B. and Kaya, Y. 2010. Modeling insect-egg data with excess zero using zero-inflated regression models. Hacettepe J. Math. Stat. 39(2):273-282. http://www.hjms.hacettepe.edu.tr/uploads/c879f14e-8c0d-4f30-8bfa-e28658 a8fe0b.pdf.

Zou, Y.; Hanning, J. and Young, D. S. 2021. Generalized fiducial inference on the mean of zero inflated Poisson and Poisson hurdle models. Germany. J Statistical Distributions and Applications. 8(5):1-15. https://doi.org/10.1186/s40488-021-00117-0.

Downloads

Published

2024-02-09

How to Cite

Otero-Prevost, Luis Gabriel, Juan A. Villanueva-Jiménez, Gustavo Ramírez-Valverde, Mónica C. Vargas-Mendoza, Carlos M. Becerril-Pérez, and Lauro Soto-Rojas. 2024. “Proposal to Obtain the Optimal Sample Size of Pests With an Excess of Zeros”. Revista Mexicana De Ciencias Agrícolas 15 (1). México, ME:e3618. https://doi.org/10.29312/remexca.v15i1.3618.

Issue

Vol. 15 No. 1 (2024)

Section

Articles

License

Copyright (c) 2024 Revista Mexicana de Ciencias Agrícolas

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

The authors who publish in Revista Mexicana de Ciencias Agrícolas accept the following conditions:

In accordance with copyright laws, Revista Mexicana de Ciencias Agrícolas recognizes and respects the authors’ moral right and ownership of property rights which will be transferred to the journal for dissemination in open access. Invariably, all the authors have to sign a letter of transfer of property rights and of originality of the article to Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP) [National Institute of Forestry, Agricultural and Livestock Research]. The author(s) must pay a fee for the reception of articles before proceeding to editorial review.      

All the texts published by Revista Mexicana de Ciencias Agrícolas —with no exception— are distributed under a Creative Commons License Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), which allows third parties to use the publication as long as the work’s authorship and its first publication in this journal are mentioned.

The author(s) can enter into independent and additional contractual agreements for the nonexclusive distribution of the version of the article published in Revista Mexicana de Ciencias Agrícolas (for example include it into an institutional repository or publish it in a book) as long as it is clearly and explicitly indicated that the work was published for the first time in Revista Mexicana de Ciencias Agrícolas.

For all the above, the authors shall send the Letter-transfer of Property Rights for the first publication duly filled in and signed by the author(s). This form must be sent as a PDF file to: revista_atm@yahoo.com.mx; cienciasagricola@inifap.gob.mx; remexca2017@gmail.

 

This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 International license.