Spatial Analysis of Environmental Factors for Modeling Plant Hopper Potential Risk Prediction

• Autorzy: Minh Vo Quang , Quang Truong Chi , Hieu Pham Thi Minh

Identyfikatory

DOI

10.12912/27197050/192320

• Języki publikacji: EN

Abstrakty

EN

Agricultural insect pests reduce crop productivity, causing a gap between global food demand and production. Early detection and early response can improve pest control efficiency. The study aimed to investigate the spatial correlations between brown plant hopper (BPH) occurrence and affected factors using field data collection in Can Tho City, Vietnam. The data on cultivation practices and meteorological conditions at 120 weekly monitoring sites at Can Tho city during the rice cropping season of 2016–2017 were collected to find the correlation between the occurrence frequency and density of BPH. Besides, GIS and spatial interpolation were applied to assess the current status of harmful situations, predict the impact trends of crop pests or diseases in space and time to serve a community’s needs, as well as forecast plant protection. As a result, in the 2nd rice cropping stage, the population of brown planthoppers was found to be highly significantly influenced by the following factors: (1) planthopper age, (2) natural enemy density, (3) air temperature, (4) field water level, and (5) number of leaves, which is highly positively correlated with brown hopper density. There is a lower correlation between leaf color code (6) and air humidity (7) and a negative correlation between pesticides used (8). The variables of rice leaf color code (6) and air humidity (7) correlate with the BPH population, although the field water level (4) and leaf count (5) do not correlate for the whole crop. It can be used to predict the changing trend of BPH in rice fields. However, the factors influencing the brown planthopper would determine the accuracy of the prognosis.

Słowa kluczowe

EN

geographic information system; potential risk; geostatistic; interpolation; brown plant hopper

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2024
• Tom: Vol. 25, iss. 11
• Strony: 110--117
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Minh Vo Quang

• Department of Land Resources, College of Environment and Natural Resources, Can Tho University, Can Tho, 90000, Viet Nam

• https://orcid.org/0000-0001-8574-7151

autor

Quang Truong Chi

• Department of Land Resources, College of Environment and Natural Resources, Can Tho University, Can Tho, 90000, Viet Nam

• https://orcid.org/0000-0002-1496-4519

autor

Hieu Pham Thi Minh

• Plant Cultivation and Protection Sub-Department of Can Tho City, Can Tho, 90000, Viet Nam

• https://orcid.org/0000-0001-5890-2101

Bibliografia

• 1. Acharya, L., Jin, L., Collins, W. 2018. College life is stressful today – Emerging stressors and depressive symptoms in college students. Journal of American College Health, 66, 01–33. https://doi.org/10.1080/07448481.2018.1451869
• 2. Akrivou, A., Georgopoulou, I., Papachristos, D.P., Milonas, P.G., Kriticos, D.J. 2021. Potential global distribution of Aleurocanthus woglumi considering climate change and irrigation. PLOS ONE, 16(12), e0261626. https://doi.org/10.1371/journal.pone.0261626
• 3. Andrewartha, H.G., Birch, L.C. 1986. The Ecological Web: More on the Distribution and Abundance of Animals. University of Chicago Press.
• 4. BioMedware, Inc., Goovaerts, P. 2019. Kriging Interpolation. Geographic Information Science & Technology Body of Knowledge. https://doi.org/10.22224/gistbok/2019.4.4
• 5. Biswas, A., Si, B.C., Biswas, A., Si, B.C. 2013. Model averaging for semi-variogram model parameters. in advances in agrophysical research. IntechOpen. https://doi.org/10.5772/52339
• 6. Bradshaw, C.D., Hemming, D., Baker, R., Everatt, M., Eyre, D., Korycinska, A. 2019. A novel approach for exploring climatic factors limiting current pest distributions: A case study of Bemisia tabaci in north-west Europe and assessment of potential future establishment in the United Kingdom under climate change. PLOS ONE, 14(8), e0221057. https://doi.org/10.1371/journal.pone.0221057
• 7. Dengmasa, M., Tongkumchum, P., Ma-A-Lee, A. 2022. Modeling MODIS land surface temperature change in Antarctica from 2000 to 2019 using cubic spline model (CMU165481). Article CMU165481. https://doi.org/10.12982/CMUJNS.2022.051
• 8. Dobesberger, E.J. 2002. Multivariate techniques for estimating the risk of plant pest establishment in new environments. Presented at NAPPO International Symposium on Pest Risk Analysis, Puerto Vallarta, Mexico. http://www.nappo.org/PRA-Symposium/PDF-Final/Dobesberger.pdf , December 2003
• 9. Evan, B. 2022. The variogram basics: A visual introduction to one of the most useful geostatistical concepts. Recorder. https://www.csegrecorder.com/articles/view/the-variogram-basics-a-visual-introto-useful-geostatistical-concepts
• 10. Li, M., Zhao, Y. 2014. Chapter 10 – Applicable scope, advantages, and disadvantages of major software packages. In M. Li & Y. Zhao (Eds.), Geophysical Exploration Technology, 305–335. Elsevier. https://doi.org/10.1016/B978-0-12-410436-5.00010-1
• 11. Liu, F., Lin, B., Meng, K. 2023. Design and realization of rural environment art construction of cultural image and visual communication. International Journal of Environmental Research and Public Health, 20(5), Article 5. https://doi.org/10.3390/ijerph20054001
• 12. Long, J., Liu, Y., Xing, S., Qiu, L., Huang, Q., Zhou, B., Shen, J., Zhang, L. 2018. Effects of sampling density on interpolation accuracy for farmland soil organic matter concentration in a large region of complex topography. Ecological Indicators, 93, 562– 571. https://doi.org/10.1016/j.ecolind.2018.05.044
• 13. Matsukawa-Nakata, M., Huy Chung, N., Kobori, Y. 2019. Insecticide application and its effect on the density of rice planthoppers, Nilaparvata lugens, and Sogatella furcifera in paddy fields in the Red River Delta, Vietnam. Journal of Pesticide Science, 44(2), 129–135. https://doi.org/10.1584/jpestics.D18-080
• 14. Paramasivam, C.R. 2019. Merits and demerits of GIS and geostatistical techniques. 17–21. https://doi.org/10.1016/B978-0-12-815413-7.00002-X
• 15. Pedersen, S.M., Lind, K. 2017. Precision agriculture – From mapping to site-specific application. 1–20. https://doi.org/10.1007/978-3-319-68715-5_1
• 16. Ranjan, R., Vinayak, S. 2020. Application of remote sensing and GIS in plant disease management. In Precision Agriculture and Sustainable Crop Production, Chourasia, H.K., Acharya K., Singh, V.K. (Eds.). Today & Tomorrow’s Printers and Publishers, New Delhi-110002 (India), 509–522.
• 17. Rano, S.H., Afroz, M., Rahman, M.M. 2022. Application of GIS on monitoring of GIS agricultural. Insect pests: A review. Reviews In Food and Agriculture, 3(1), 19–23. https://doi.org/10.26480/rfna.01.2022.19.23
• 18. Szyniszewska, A.M., Akrivou, A., Björklund, N., Boberg, J., Bradshaw, C., Damus, M., Gardi, C., Hanea, A., Kriticos, J., Maggini, R., Musolin, D.L., MacLeod, A. 2024. Beyond the present: How climate change is relevant to pest risk analysis. EPPO Bulletin, 54(S1), 20–37. https://doi.org/10.1111/epp.12986
• 19. Weinberg, J., Ota, N., Goergen, G., Fagbohoun, J.R., Tepa-Yotto, G.T., Kriticos, D.J. 2022. Spodoptera eridania: Current and emerging crop threats from another invasive, pesticide-resistant moth. Entomologia Generalis, 701–712. https://doi.org/10.1127/entomologia/2022/1397
• 20. Western, A.W., Blöschl, G. 1999. On the spatial scaling of soil moisture. Journal of Hydrology, 217(3–4), 203–224. https://doi.org/10.1016/S0022-1694(98)00232-7