Development and validation of mathematical model for population estimation of the two-spotted spider mite in soybeans

Ezequiel G. Souza; Antonio J. Steidle-Neto; Daniela C. Lopes; Priscilla T. N. Maia; Marcos A. M. Fadini

doi:10.37486/2675-1305.ec06031

Authors

Ezequiel G. Souza Universidade Federal de São João del-Rei, Sete Lagoas, MG, Brazil https://orcid.org/0000-0001-7638-0183
Antonio J. Steidle-Neto Universidade Federal de São João del-Rei, São João del-Rei, MG, Brazil https://orcid.org/0000-0003-3125-8330
Daniela C. Lopes Universidade Federal de São João del-Rei, São João del-Rei, MG, Brazil https://orcid.org/0000-0003-2612-3140
Priscilla T. N. Maia Universidade Federal de São João del-Rei, São João del-Rei, MG, Brazil https://orcid.org/0000-0002-5759-9961
Marcos A. M. Fadini Universidade Federal de São João del-Rei, São João del-Rei, MG, Brazil https://orcid.org/0000-0001-6471-1600

DOI:

https://doi.org/10.37486/2675-1305.ec06031

Keywords:

Sampling, Glycine max, Tetranychus urticae, colorimetry

Abstract

The two-spotted spider mite Tetranychus urticae Koch, 1836 (Acari: Tetranychidae) causes bronzing and leaf drop in soybeans, reducing grain production and quality. Using colorimetric measurements in soybean-producing areas can be an alternative for monitoring the mite, aiding in the estimation of field populations. The objective of this study was to estimate the quantity of the two-spotted spider mite in soybeans through colorimetric measurements using the CIE-L*a*b* color space. Soybean plants at the V3 stage were used with 4 treatments (Control=0; T1=30; T2=60; and T3=100 adult female T. urticae per plant), 4 repetitions, and 170 measurements. Regression and variance analyses were performed using Origin Pro 2015 software. Prediction equations were calibrated. Our results show that there was a significant difference for the L*, a*, and ΔE* variables. Regression analyses presented determination coefficient values of 0.87, 0.93, and 0.55 for L*, a*, and b*, respectively. The use of the (a*) variable in the CIE-L*a*b* color space proved effective in distinguishing between infestation levels. The model created to estimate the quantity of mites on soybean leaves has the potential to be applied in soybean production areas with infestations of the two-spotted spider mite.

Downloads

Download data is not yet available.

References

Amarante, C. V. T.; Zanardi, O. Z.; Miqueloto, A.; Steffens, C. A.; Erhart, J.; De Almeida, J. A. (2009) Non-destructive quantification of area and chlorophyll Content in the leaves of young 'cabernet sauvignon' grapevines. Revista Brasileira de Fruticultura, 3(1): 680-686. doi: 10.1590/S0100-29452009000300009

Cáceres, D.; Díaz, M.; Shinya, P.; Infante, R. (2016) Assessment of peach internal flesh browning through colorimetric measures. Postharvest Biology and Technology, 111: 48-52. doi: 10.1016/j.postharvbio.2015.07.007

CIE (1986) Standard colorimetric observers. Publication CIE No. S2. Bureau of the Commission Internationale de l'Eclairage, Austria.

De Araújo, C. R. N.; Do Amaral, F. H. F.; Brandão, I. R. S.; Pereira, T. E. R.; Santos, V. C. (2022) The Transversality of the environmental theme: The use of pesticides in the planting of soybeans and the impacts on biodiversity. International Journal Semiarid, 5(5): 77-91. doi: 10.56346/journal_semiarid_ijsa.v5i5.116

De Paula, M. H.; De Mesquita, R. R. S.; Gonçalez, J. C.; Ribeiro, E. S.; Souza, R. S. (2016) Utilização de métodos não destrutivos para caracterização simplificada da madeira de cumaru (Dipteryx odorata Willd). Biodiversidade, 15: 2-12.

Francis, F. J. (1980) Color quality evaluation of horticultural crops. HortScience, 15(1): 58-59. doi: 10.21273/hortsci.15.1.58

Fraulo, A. B.; Cohen, M.; Liburd, O. E. (2009) Visible/near infrared reflectance (VNIR) spectroscopy for detecting twospotted spider mite (Acari: Tetranychidae) damage in strawberries. Environmental Entomology, 38(1): 137-142. doi: 10.1603/022.038.0117

Herrmann, I.; Berenstein, M.; Sade, A.; Karnieli, A.; Bonfil, D. J.; Weintraub, P. G. (2012) Spectral monitoring of two-spotted spider mite damage to pepper leaves. Remote Sensing Letters, 3(4): 277-283. doi: 10.1080/01431161.2011.576709

Iatrou, G.; Cook, C. M.; Stamou, G.; Lanaras, T. (1995) Chlorophyll fluorescence and leaf chlorophyll content of bean leaves injured by spider mites (Acari: Tetranychidae). Experimental & Applied Acarology, 19(1): 581-591. doi: 10.1007/bf00048813

Jakubowska, M.; Dobosz, R.; Zawada, D.; Kowalska, J. (2022) A Review of crop protection methods against the twospotted spider mite-Tetranychus urticae Koch (Acari: Tetranychidae) with special reference to alternative methods. Agriculture, 12(7): 898. doi: 10.3390/agriculture12070898

Lan, Y.; Zhang, H.; Hoffmann, W. C.; Lopez, J. J. D. (2013) Spectral response of spider mite infested cotton: Mite density and miticide rate study. International Journal of Agricultural and Biological Engineering, 6(1): 48-52.

Luedeling, E.; Hale, A.; Zhang, M.; Bentley, W. J.; Dharmasri, L. C. (2009) Remote sensing of spider mite damage in California peach orchards. International Journal of Applied Earth Observation and Geoinformation, 11(4): 244-255. doi: 10.1016/j.jag.2009.03.002

McGuire, R. G. (1992) Reporting of objective color measurements. HortScience, 27(12): 1254-1255. doi: 10.21273/hortsci.27.12.1254

Meir, S.; Philosoph-Hadas, S.; Gloter, P.; Aharoni, N. (1992) Nondestructive assessment of chlorophyll content in watercress leaves by a tristimulus reflectance colorimeter. Postharvest Biology and Technology, 2(2): 117-124. doi: 10.1016/0925-5214(92)90014-g

Nansen, C. (2016) The potential and prospects of proximal remote sensing of arthropod pests. Pest Management Science, 72(4): 653-659. doi: 10.1002/ps.4209

Padilha, G.; Fiorin, R. A.; Cargnelutti Filho, A.; Pozebon, H.; Rogers, J.; Marques, R. P.; Castilhos, L. B.; Donatti, A.; Stefanelo, L.; Burtet, L. M., et al. (2020) Damage assessment and economic injury level of the two-spotted spider mite Tetranychus urticae in soybean. Pesquisa Agropecuária Brasileira, 55: 01836. doi: 10.1590/S1678-3921.pab2020.v55.01836

Pedigo, L. P.; Rice, M. E. (2009) Entomology and pest management. 6th ed. Prentice Hall: New Jersey.

R Core Team (2018) R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical. Computing

Roggia, S.; Guedes, J. V. C.; Kuss, R. C. R.; Arnemann, J. A.; Návia, D. (2008) Spider mites associated to soybean in Rio Grande do Sul, Brazil. Pesquisa Agropecuária Brasileira, 4(3): 295-301. doi: 10.1590/s0100-204X2008000300002

Ustin, S. L.; Jacquemoud, S. (2020) How the optical properties of leaves modify the absorption and scattering of energy and enhance leaf functionality. In: Cavender-Bares, J., Gamon, J. A.; Townsend, P. A. (Eds.), Remote sensing of plant biodiversity, p. 349-384. Springer: Cham. doi: 10.1007/978-3-030-33157-3_14

Vilhalva, D. A. A.; Soares Júnior, M. S.; Caliari, M.; Silva, F. A. D. (2012) Secagem convencional de casca de mandioca proveniente de resíduos de indústria de amido. Pesquisa Agropecuária Tropical, 42(3): 331-339. doi: 10.1590/s1983-40632012000300003

Vodyanitskii, Y. N.; Kirillova, N. P. (2016) Application of the CIE-L* a* b* system to characterize soil color. Eurasian Soil Science, 49(11): 1259-1268. doi: 10.1134/S1064229316110107