Geographical Variation, Path Diagram, and Regression Tree of the Incidence and Severity of Potato Late Blight (Phytophthora infestans): The Observed Pattern in the Major Growing Areas in Benguet Province
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Jun 27, 2025
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Abstract
There are various multivariate statistical tools for plant disease epidemics to associate multiple epidemiological factors represented by the disease triangle and tetrahedron. However, its applications are limited in Asian countries, let alone in the locality. This descriptive causal–comparative [farm] survey research, therefore, utilized Cluster Analysis, Path Analysis, and Classification and Regression Tree (C&RT). These multivariate tools were used to assess the geographical variations, account the explained variance/ effect size, and model the causal relationship of multiple variables on host variables, environmental factors, and cultural management practices of farmers to visually assessed incidence and severity of late blight of potato farms in major growing areas in Benguet observed in May 2021. There are three (3) clusters of the observed farms that emerged, of which cluster 3 has consistently had the lowest incidence and severity of potato late blight observed. Therefore, the host characteristics and cultural management observed in cluster 3 are worthy of consideration in potato production. Both the best/tuned regression trees and the combined recursive path diagram showed host variables to have contributed a significantly high proportion of effect/ explained variance and emerged most of the significant predictors for the observed incidence and severity of potato late blight.
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Van Maanen, A., & X.-M. XU. (2003). Modelling plant disease epidemics. European Journal of Plant Pathology. European Journal of Plant Pathology, 109(7): 669–682. https://doi.org/10.1023/a:1026018005613
Xu, X. (2006). Modelling and interpreting disease progress in time. The Epidemiology of Plant Diseases, 215–238. https://doi.org/10.1007/14020-4581-6_8
Bentler, P.M., & Chou, C.H. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16, 78-117.
Bondari, K. (1990). Path analysis in agricultural research. Conference on Applied Statistics in Agriculture. https://doi.org/10.4148/2475-7772.1439
Boomsma, A. (1985). Nonconvergence, improper solutions, and starting values in LISREL maximum likelihood estimation. Psychometrika, 50, 229-242.
Boomsma, A. (1982). Robustness of LISREL against small sample sizes in factor analysis models. In K. G. Joreskog & H. Wold (Eds.), Systems under indirection observation: Causality, structure, prediction (Part I) (pp. 149-173). Amsterdam, Netherlands: North Holland.
Cruickshank, G., Stewart, H.E., & Wastie, R.L. (1982). An illustrated assessment key for foliage blight of potatoes. Potato Research, 25(2): 213-214. https://doi.org/10.1007/bf02359807
Cunniffe, N.J., Koskella, B., Metcalf, C.J.E., Parnell, S., Gottwald, T.R., & Gilligan, C.A. (2015). Thirteen challenges in modelling plant diseases. Epidemics, 10, 6–10. https://doi.org/10.1016/j.epidem.2014.06.002
Food and Agriculture Organization Corporate Statistical Database. (2021). Food and Agriculture Organization of the United Nation. http://www.fao.org/faostat/en/#data/QC.
Francl, L.J. (2001). The Disease Triangle: A plant pathological paradigm revisited. The Plant Health Instructor. https://doi.org/10.1094/phi-t-20010517-01
Gonzales, I.C., Kiswa, C.G., & Bautista, A.B. (2016). Sustainable potato production in the Philippine Cordillera Region. International Journal of Engineering and Applied Sciences, 3(6): 29–37.
Hosack, P., & Miller, L. (n.d.). Preventing and Managing Plant Diseases. MU Extension. https://extension.missouri.edu/publications/mg13
Hu, L., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1): 1–55. https://doi.org/10.1080/10705519909540118
International Business Machines Corporation. (2016). Two-step cluster analysis. https://www.ibm.com/docs/SSLVMB_23.0.0/spss/base/idh_twostep_main.html.
Jeger, M.J. (2004). Analysis of disease progress as a basis for evaluating disease management practices. Annual Review of Phytopathology, 42(1): 61–82. https://doi.org/10.1146/annurev.phyto.42.040803.140427
Johnson, D.A., & Cummings, T.F. (2021). Canopy environment in irrigation a key factor for managing late blight in semiarid environments. Spudman. https://spudman.com/article/canopyenvironment-in-irrigation-a-key-factor-formanaging-late-blight-in-semiarid-environments/
Lewis, R.J. (2000). An introduction to classification and regression tree (CART) analysis [Paper Presentation]. Annual Meeting of the Society for Academic Emergency Medicine, San Francisco, California.
Lleras, C. (2005). Path analysis. In Encyclopedia of Social Measurements (Vol. 3, pp. 25–30). Elsevier Inc.
Madden, L.V., & Hughes, G. (1995). Plant Disease Incidence: Distributions, heterogeneity, and temporal analysis. Annual Review of Phytopathology, 33(1), 529–564. https://doi.org/10.1146/annurev.py.33.090195.002525
Maloy, O.C. (2005). Plant Disease Management. The Plant Health Instructor. https://doi.org/10.1094/phi-i-2005-0202-01
Mendes, J., Pinho, T.M., Neves Dos Santos, F., Sousa, J.J., Peres, E., Boaventura-Cunha, J., Cunha, M., & Morais, R. (2020). Smartphone applications targeting precision agriculture practices - A Systematic Review. Agronomy, 10(6): 855. https://doi.org/10.3390/agronomy10060855
Mohamed, N., & Awang, S.R. (2015). The multiple intelligence classification of management graduates using two step cluster analysis. Malaysian Journal of Fundamental and Applied Sciences, 11(1). https://doi.org/10.11113/mjfas.v11n1.351
Moisen, G.G. (2008). Classification and regression trees. In: Jorgensen, Sven Erik; Fath, Brian D., eds. Encyclopedia of Ecology, Volume 1. Oxford, U.K.: Elsevier. p. 582-588.
Morgan, J. (2014). Classification and regression tree analysis. https://www.bu.edu/sph/files/2014/05/MorganCART.pdf.
Nayak, P., Mukherjee, A.K., Pandit, E., & Pradhan, S.K. (2018). Application of statistical tools for data analysis and interpretation in rice plant pathology. Rice Science, 25(1): 1–18. https://doi.org/10.1016/j.rsci.2017.07.001
Nemes, Z., Baciu, A., Popa, D., Mike, L., PetrusVancea, A., & Danci, O. (2008). The study of the potato’s life–cycle phases important to the increase of the individual variability. Analele Universitatii din Oradea, Fascicula Biologie, Tom, XV, 60-63.
Nunnally, J.C. (1967). Psychometric theory. New York, NY: McGraw-Hill.
Nutter, F.F.W. (2007). The Role of plant disease epidemiology in developing successful integrated disease management programs. General Concepts in Integrated Pest and Disease Management, 45–79. https://doi.org/10.1007/978-1-4020-6061-8_3
Patrignani, A., & Ochsner, T.E. (2015). Canopeo: A powerful new tool for measuring fractional green canopy cover. Agronomy Journal, 107(6): 2312–2320. https://doi.org/10.2134/agronj15.0150
Philippine Statistics Authority OPENSTAT. (2021). Philippine Statistics Authority. https://openstat.psa.gov.ph/PXWeb/pxweb/en/DB/DB__2E__CS/?tablelist=true&rxid=bdf9d8da-96f1-4100ae09-18cb3eaeb313.
Rupavatharam, S., Kennepohl, A., Kummer, B., & Parimi, V. (2018) Automated plant disease diagnosis using innovative android app (Plantix) for farmers in Indian state of Andhra Pradesh. Phytopathology, 108 (10). http://oar.icrisat.org/11014/
Ristaino, J., Schumann, G.L., & D’arcy, C.J. (2000). Late blight of potato and tomato. The Plant Health Instructor. https://doi.org/10.1094/phi-i2000-0724-01
Thinsungnoen, T., Kaoungku, N., Durongdumronchai, P., Kerdprasop, K., & Kerdprasop, N. (2015). The Clustering Validity with Silhouette and Sum of Squared Errors. The Proceedings of the 2nd International Conference on Industrial Application Engineering 2015. https://doi.org/10.12792/iciae2015.012
Turechek, W.W. (2004). Nonparametric tests in plant disease epidemiology: Characterizing disease Associations. Phytopathology, 94(9), 1018–1021. https://doi.org/10.1094/phyto.2004.94.9.1018
Van Maanen, A., & X.-M. XU. (2003). Modelling plant disease epidemics. European Journal of Plant Pathology. European Journal of Plant Pathology, 109(7): 669–682. https://doi.org/10.1023/a:1026018005613
Xu, X. (2006). Modelling and interpreting disease progress in time. The Epidemiology of Plant Diseases, 215–238. https://doi.org/10.1007/14020-4581-6_8