Spatial Interpolation of Foliar Diseases in Oil Palm Nurseries: a Methodological Approach

Authors

DOI:

https://doi.org/10.15517/d7e20z98

Keywords:

Foliar Diseases, Inverse Distance Weighted (IDW), Severity, Spatial Analysis, Validation

Abstract

The use of simple interpolation techniques for the diagnosis of foliar disease can improve timely agronomic management in crop systems. Currently, there is a lack of calibrated and validated tools to accurately apply interpolation methods in agricultural environments, according to the spatial distribution of foliar diseases.
This study aims to identify the most appropriate algorithm for interpolating foliar diseases and determining the optimal sample size for cross-validation. A case study was conducted using the leaf severity percentage in oil palm nurseries. This focused on analyzing the performance of the following interpolators: Triangulation, Inverse Distance Weighted (IDW), Natural Neighbor, Cubic Spline, and Ordinary Kriging. Additionally, various sample sizes for cross-validation were examined, with ranges from 2.5 % to 30 %, using the I Moran and Power analysis as metrics. It was found that, when the sample size reached or exceeded 10 % of the total dataset, spatial autocorrelation decreased, and thus the performance of the interpolation method became more critical for prediction.
The study concluded that IDW was the most effective interpolation method (α = 0.05) for predicting the spatial distribution of a foliar disease, outperforming the other evaluated algorithms.

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References

[1] W. Luo et al., “An improved regulatory sampling method for mapping and representing plant disease from a limited number of samples”, Epidemics, vol. 4, no. 2, pp. 68-77, 2012, doi: 10.1016/j.epidem.2012.02.001.

[2] D. Shepard, “A two-dimensional interpolation function for irregularly-spaced data”, en Proceedings of the 1968 23rd ACM national conference, ene. 1968, pp. 517-524.

[3] D. G. Krige, “A statistical approach to some basic mine valuation problems on the Witwatersrand”, J. South Afr. Inst. Min. Metall., vol. 52, no. 6, pp. 119-139, dic. 1951.

[4] M. V. Micca, N. R. Andrada y A. S. Larrusse, “Análisis exploratorio espacial de tizón común exserohilum turcicum (Leonard and Suggs) en estratos foliares de maíz en Villa Mercedes, San Luis”, FAVE Sección Ciencias Agrarias, vol. 14, no. 2, pp. 111-122, jun. 2016, doi: 10.14409/fa.v14i2.5724.

[5] N. J. Cárdenas Pardo, A. E. Darghan Contreras, M. D. Sosa Rico y A. Rodríguez, “Análisis espacial de la incidencia de enfermedades en diferentes genotipos de cacao (Theobroma cacao L.) en El Yopal (Casanare), Colombia”, Acta Biolo. Colomb., vol. 22, no. 2, pp. 209-220, may. 2017, doi: 10.15446/abc.v22n2.61161.

[6] A. Tapia-Rodríguez, J. F. Ramírez-Dávila, D. K. Figueroa-Figueroa, M. L. Salgado-Siclan y R. Serrato-Cuevas, “Spatial analysis of anthracnose in avocado cultivation in the State of Mexico”, Rev. Mexic. Fitopatol., vol. 38, no. 1, pp. 132-145, dic. 2019, doi: 10.18781/R.MEX.FIT.1911-1.

[7] J. A. Pizzato et al., “Geostatistics as a Methodology for Studying the Spatiotemporal Dynamics of Ramularia areola in Cotton Crops”, Am. J. Plant Sci., vol. 05, no. 15, pp. 2472-2479, jul. 2014, doi: 10.4236/ ajps.2014.515262.

[8] M. de Carvalho Alves y E. A. Pozza, “Indicator kriging modeling epidemiology of common bean anthracnose”, Appl. Geomatics, vol. 2, no. 2, pp. 65-72, jun. 2010, doi: 10.1007/s12518-010-0021-1.

[9] M. C. Alves, E. A. Pozza, J. C. Machado, D. V. Araújo, V. Talamini y M. S. Oliveira, “Geoestatística como metodologia para estudar a dinâmica espaço-temporal de doenças associadas a Colletotrichum spp. transmitidos por sementes”, Fitopatol. Bras., vol. 31, no. 6, pp. 557-563, dic. 2006, doi: 10.1590/S0100-41582006000600004.

[10] M. de Carvalho Alves, F. M. da Silva, E. A. Pozza y M. S. de Oliveira, “Modeling spatial variability and pattern of rust and brown eye spot in coffee agroecosystem”, J. Pest. Sci., vol. 82, no. 2, pp. 137-148, may. 2009, doi: 10.1007/ s10340-008-0232-y.

[11] M. R. Nelson, T. V. Orum, R. Jaime-Garcia y A. Nadeem, “Applications of Geographic Information Systems and Geostatistics in Plant Disease Epidemiology and Management”, Plant. Dis., vol. 83, no. 4, pp. 308-319, abr. 1999, doi: 10.1094/PDIS.1999.83.4.308.

[12] L. V. Madden y G. Hughes, “Plant Disease Incidence: Distributions, Heterogeneity, and Temporal Analysis”, Annu. Rev. Phytopathol., vol. 33, no. 1, pp. 529-564, sep. 1995, doi: 10.1146/annurev.py.33.090195.002525.

[13] R. Jaime-Garcia, T. V. Orum, R. Felix-Gastelum, R. Trinidad-Correa, H. D. VanEtten y M. R. Nelson, “Spatial Analysis of Phytophthora infestans Genotypes and Late Blight Severity on Tomato and Potato in the Del Fuerte Valley Using Geostatistics and Geographic Information Systems”, Phytopathology, vol. 91, no. 12, pp. 1156-1165, dic. 2001, doi: 10.1094/PHYTO.2001.91.12.1156.

[14] G. Garbanzo, E. Molina, G. Cabalceta, and F. Ramírez, “Evaluación de Si y Ca foliar en el crecimiento y tolerancia de complejo de necrosis foliar en palma aceitera”, Agronomía Costarricense, vol. 42, no. 2, jun. 2018, doi: 10.15517/rac.v42i2.33777.

[15] N. Panigrahi, “Spatial Interpolation Techniques”, en Computing in Geographic Information Systems. Londres, Reino Unido: Taylor & Francis Group, 2014, pp. 155-167.

[16] E. Stefanakis, Geographic Databases and Information Systems. Los Ángeles, CA, Estados Unidos: CreateSpace Independent Publishing Platform, 2014.

[17] H. Ledoux yC. Gold, “An Efficient Natural Neighbour Interpolation Algorithm for Geoscientific Modelling”, en Developments in Spatial Data Handling, 2005, pp. 97-108. doi: 10.1007/3-540-26772-7_8.

[18] M. A. Ramadan, I. F. Lashien y W. K. Zahra, “Polynomial and nonpolynomial spline approaches to the numerical solution of second order boundary value problems”, Appl. Math. Comput., vol. 184, no. 2, pp. 476-484, ene. 2007, doi: 10.1016/j.amc.2006.06.053.

[19] J. Haber, F. Zeilfelder, O. Davydov y H.-P. Seidel, “Smooth approximation and rendering of large scattered data sets”, en Proceedings Visualization, 2001, pp. 341-571. doi: 10.1109/ VISUAL.2001.964530.

[20] R. Sunila y K. Kollo, “Kriging and Fuzzy Approaches for DEM”, en Quality aspects in spatial data mining, A. Stein, W. Shi y W. Bijker, Eds., Londres, Reino Unido: Taylor & Francis Group, 2009, pp. 102-114.

[21] M. Goodchild, Spatial autocorrelation. Norwich, Reino Unido: Geo Book, 1986.

[22] R. B. Bausell y Y.-F. Li, Power analysis for experimental research: a practical guide for the biological, medical, and social sciences. Cambridge, Reino Unido: Cambridge University Press, 2002.

[23] J. Cohen, Statistical Power Analysis for the Behavioral Sciences Second Edition, vol. 2. Mahwah, NJ, Estados Unidos: Lawrence Erlbaum Associates, 1988.

[24] C. A. Schloeder, N. E. Zimmerman y M. J. Jacobs, “Division S-8—nutrient management & soil & plant analysis: comparison of methods for interpolating soil properties using limited data”, Soil Sci. Soc. Am. J., vol. 65, no. 2, pp. 470-479, 2001, doi: 10.2136/sssaj2001.652470x.

[25] R: A Language and Environment for Statistical Computing (2024), R Foundation for Statistical Computing. Accesado: mar. 30, 2024. [En línea]. Disponible en: https://www.R-project.org

[26] D. L. Phillips y D. G. Marks, “Spatial uncertainty analysis: propagation of interpolation errors in spatially distributed models”, Ecol. Modell., vol. 91, no. 1, pp. 213-229, nov. 1996, doi: 10.1016/0304-3800(95)00191-3.

[27] A. M. Molinaro, R. Simon y R. M. Pfeiffer, “Prediction error estimation: a comparison of resampling methods”, Bioinformatics, vol. 21, no. 15, pp. 3301-3307, ago. 2005, doi: 10.1093/bioinformatics/bti499.

[28] R. K. Meentemeyer, B. L. Anacker, W. Mark y D. M. Rizzo, “Early detection of emerging forest disease using dispersal estimation and ecological niche modeling”, Ecological Applications, vol. 18, no. 2, pp. 377-390, mar. 2008, doi: 10.1890/07-1150.1.

[29] L. Willocquet, L. Lebreton, A. Sarniguet y P. Lucas, “Quantification of within-season focal spread of wheat take-all in relation to pathogen genotype and host spatial distribution”, Plant Pathol., vol. 57, no. 5, pp. 906-915, oct. 2008, doi: 10.1111/j.1365-3059.2008.01834.x.

[30] M. M. Ndoungué Djeumekop et al., “Spatial and Temporal Analysis of Phytophthora megakarya Epidemic in Newly Established Cacao Plantations”, Plant Dis., vol. 105, no. 5, pp. 1448-1460, may. 2021, doi: 10.1094/PDIS-09-19-2024- RE.

[31] F. Hay, D. W. Heck, A. Klein, S. Sharma, C. Hoepting y S. J. Pethybridge, “Spatiotemporal Dynamics of Stemphylium Leaf Blight and Potential Inoculum Sources in New York Onion Fields”, Plant Dis., vol. 106, no. 5, pp. 1381-1391, may. 2022, doi: 10.1094/PDIS-07-21-1587-RE.

[32] G. Garnero y D. Godone, “Comparisons between different interpolation techniques”, Int. Arch. of the Photogramm. Remote Sens. Spatial Inf. Sci., vol. 40, pp. 139-144, ene. 2014, doi: 10.5194/isprsarchives-XL-5-W3-139-2013.

[33] P. Balanagouda et al., “Assessment of the spatial distribution and risk associated with fruit rot disease in Areca catechu L.”, J. Fungi, vol. 7, no. 10, p. 797, sep. 2021, doi: 10.3390/jof7100797.

[34] V. R. Joseph, “Optimal ratio for data splitting”, Stat. Anal. Data Min.: ASA Data Sci. J., vol. 15, no. 4, pp. 531-538, ago. 2022, doi: 10.1002/sam.11583.

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Published

2025-10-20

Issue

Vol. 35 No. 2 (2025): July - December 2025 (open issue)

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Copyright (c) 2025 Jaime Garbanzo-León, Gabriel Garbanzo León (Autor/a)

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