Published
Climate-driven modelling of potato diseases:Approach to evidence-based decision system
Modelación de enfermedades de papa mediada por variables climáticas: enfoque para un sistema de decisiones basado en evidencia
DOI:
https://doi.org/10.15446/agron.colomb.v44n1.125209Keywords:
artificial intelligence, weather, plant health, early warning systems, deterministic models, frequentist models, Bayesian models (en)inteligencia artificial, tiempo meteorológico, sanidad vegetal, sistemas de alerta temprana, modelos determinísticos, modelos frecuentistas, modelos bayesianos (es)
Downloads
Potato diseases represent a growing threat to global food security, and climate-driven disease modeling has become an essential tool for anticipating outbreaks and informing management decisions. However, forecasting tools remain inconsistently applied and often lack adaptation to local conditions. This study synthesizes recent advances in climate-based modeling of potato diseases through a combined bibliometric and perspective-based analysis, aiming to identify research gaps, characterize key climatic drivers, and propose a framework for model selection. Three modeling approaches were evaluated: deterministic, probabilistic (frequentist and Bayesian), and artificial intelligence–based methods. Temperature, relative humidity, leaf wetness duration, precipitation, and wind speed emerged as the most influential climatic drivers. Late blight remains the most extensively modeled disease worldwide, which leaves other economically important diseases underrepresented. Each modeling approach presents distinct trade-offs: deterministic models offer biological interpretability but require calibration before transfer to production contexts; frequentist models depend on statistical assumptions (independence, homoscedasticity, and low multicollinearity), which are often violated in biological systems; Bayesian models provide greater flexibility for handling uncertainty and integrating limited or heterogeneous datasets, although reliable still depend on high-quality data; and artificial intelligence methods effectively capture nonlinear interactions but require large, balanced datasets and raise concerns regarding interpretability and overfitting. A geographical gap was identified in Colombia, where climate-based disease modeling remains scarce despite the country’s importance as a potato-producing region. These findings emphasize the need for context-sensitive model selection, rigorous data quality control, and transparent validation practices to support more reliable disease forecasting systems and evidence-based crop management.
Las enfermedades de la papa representan una amenaza creciente para la seguridad alimentaria, y la modelización de enfermedades basadas en el clima se ha convertido en una herramienta esencial para anticipar brotes y decisiones de manejo. Sin embargo, las herramientas de pronóstico se aplican de forma inconsistente y carecen de adaptación local. Este estudio sintetiza avances recientes en la modelización climática de enfermedades de la papa mediante un análisis bibliométrico y de perspectiva, con el fin de identificar vacíos de investigación, caracterizar los principales factores climáticos y proponer un marco para la selección de modelos. Se evaluaron tres enfoques: determinísticos, probabilísticos (frecuentistas y bayesianos) y basados en inteligencia artificial. La temperatura, la humedad relativa, la duración de la humedad, la precipitación y la velocidad del viento resultaron los factores más influyentes. El tizón tardío sigue siendo la enfermedad más modelada mundialmente, dejando subrepresentadas otras enfermedades de importancia económica. Cada enfoque presenta ventajas y limitaciones: los determinísticos ofrecen interpretabilidad biológica pero requieren calibración previa antes de transferirse al contexto productivo; los frecuentistas dependen de supuestos estadísticos (independencia, homocedasticidad, baja multicolinealidad) frecuentemente violados en sistemas biológicos; los bayesianos ofrecen mayor flexibilidad ante la incertidumbre y datos limitados o heterogéneos, aunque dependen de datos de calidad; y la inteligencia artificial captura interacciones no lineales, pero requiere grandes conjuntos de datos balanceados y plantea dudas sobre interpretabilidad y sobreajuste. Se identificó un vacío geográfico en Colombia, donde esta modelización es escasa pese a su relevancia como productor de papa. Estos hallazgos resaltan la necesidad de selección de modelos sensibles al contexto, control de calidad de datos y validación. Estos hallazgos resaltan la necesidad de una selección de modelos adaptados al contexto, un control riguroso de la calidad de los datos y prácticas de validación transparentes para respaldar unos sistemas de predicción de enfermedades más fiables y una gestión de los cultivos basada en la evidencia.
References
Abderrahmane, O., Berdja, R., Ammad, F., Bensaci, O. A., & Benchabane, M. (2022). Potato late blight (Phytophthora infestans) disease forecasting using an auto-encoded long short-term memory recurrent neural networks in North-Western Algeria. Archives of Phytopathology and Plant Protection, 55(13), 1542−1557. https://doi.org/10.1080/03235408.2022.2105639
Agrios, G. N. (2005). Plant pathology (5th ed.). Elsevier Academic Press. https://doi.org/10.1016/B978-0-08-047378-9.50002-6
Ali, Y., Raza, A., Aatif, H. M., Ijaz, M., Ul-Allah, S., ur Rehman, S., Mahmoud, S. Y. M., Farrag, E. S. H., Amer, M. A., & Moustafa, M. (2022). Regression modeling strategies to predict and manage potato leaf roll virus disease incidence and its vector. Agriculture, 12(4), Article 550. https://doi.org/10.3390/agriculture12040550
Alwosheel, A., van Cranenburgh, S., & Chorus, C. G. (2018). Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. Journal of Choice Modelling, 28, 167–182. https://doi.org/10.1016/j.jocm.2018.07.002
Bagchi, P., Sawicka, B., Stamenkovic, Z., Marković, D., & Bhattacharjee, D. (2024). Potato late blight outbreak: a study on advanced classification models based on meteorological data. Sensors, 24(23), Article 7864. https://doi.org/10.3390/s24237864
Baker, K. M., & Kirk, W. W. (2007). Comparative analysis of models integrating synoptic forecast data into potato late blight risk estimate systems. Computers and Electronics in Agriculture, 57(1), 23−32. https://doi.org/10.1016/j.compag.2007.01.011
Betancourth, C., Portilla, E., & Salas, H. (2008). Evaluación de la reacción de nueve genotipos de papa (Solanum tuberosum subsp. Andigena) al ataque de Phytophthora infestans (Mont) de Bary. Agronomía Colombiana, 26(3), 411−416. https://revistas.unal.edu.co/index.php/agrocol/article/view/11472
Blandón-Díaz, J. U., Forbes, G. A., Andrade-Piedra, J. L., & Yuen, J. E. (2011). Assessing the adequacy of the simulation model LATEBLIGHT under Nicaraguan conditions. Plant Disease, 95(7), 839−846. https://doi.org/10.1094/PDIS-10-10-0702
Cabezas Dávila, B. M. (2024). Análisis de daños causado por Ralstonia solanacearum en cultivo de papa [Undergraduate thesis, Universidad Técnica de Babahoyo, Ecuador]. http://dspace.utb.edu.ec/handle/49000/15929
Cárdenas-Urrego, W. F., Benavides-Quiroga, K. S., León-Rueda, W. A., & Ramírez-Gil, J. G. (2026). Digital platform as a tool for data management of the potato production system in Colombia. Smart Agricultural Technology, 13, Article 101808. https://doi.org/10.1016/j.atech.2026.101808
Chakraborty, S., & Newton, A. C. (2011). Climate change, plant diseases and food security: an overview. Plant Pathology, 60(1), 2−14. https://doi.org/10.1111/j.1365-3059.2010.02411.x
Chen, S., Wang, J., Xie, N., Chen, K., & Ding, Y. (2025). Plant pathology and diseases from a novel bibliometric method. Biocatalysis and Agricultural Biotechnology, 68, Article 103698. https://doi.org/10.1016/j.bcab.2025.103698
Cohen, S. P., & Leach, J. E. (2020). High temperature-induced plant disease susceptibility: more than the sum of its parts. Current Opinion in Plant Biology, 56, 235−241. https://doi.org/10.1016/j.pbi.2020.02.008
Cól, M., Coelho, M., & Ponte, E. M. (2024). Weather-based logistic regression models for predicting wheat head blast epidemics. Plant Disease, 108(7), 2206−2213. https://doi.org/10.1094/PDIS-11-23-2513-RE
Colturato, A. B., & Chavier, L. F. C. (2019). Decision support system for late blight in tomato and potato. Acta Horticulturae, 1233, 19−26. https://doi.org/10.17660/ActaHortic.2019.1233.4
Crosier, W. (1934). Studies in the biology of Phytophthora infestans (Mont.) De Bary. Memoir, 155. https://digital.library.cornell.edu/catalog/chla7251474_8568_014
Cucak, M., Sparks, A., Moral, R. A., Kildea, S., Lambkin, K., & Fealy, R. (2019). Evaluation of the ‘Irish Rules’: the potato late blight forecasting model and its operational use in the Republic of Ireland. Agronomy, 9(9), Article 515. https://doi.org/10.3390/agronomy9090515
Cunniffe, N. J., Koskella, B. E., Metcalf, C. J., Parnell, S., Gottwald, T. R., & Gilligan, C. A. (2015). Thirteen challenges in modelling plant diseases. Epidemics, Challenges in Modelling Infectious Disease Dynamics, 10, 6–10. https://doi.org/10.1016/j.epidem.2014.06.002
Dar, W. A., Parry, F. A., & Bhat, B. A. (2021). Potato late blight disease prediction using meteorological parameters in Northern Himalayas of India. Journal of Agrometeorology, 23(3), 310−315. https://doi.org/10.54386/jam.v23i3.35
De Wolf, E. D., & Isard, S. A. (2007). Disease cycle approach to plant disease prediction. Annual Review of Phytopathology, 45, 203−220. https://doi.org/10.1146/annurev.phyto.44.070505.143329
Donatelli, M., Magarey, R. D., Bregaglio, S., Willocquet, L., Whish, J. P. M., & Savary, S. (2017). Modelling the impacts of pests and diseases on agricultural systems. Agricultural Systems, 155, 213−224. https://doi.org/10.1016/j.agsy.2017.01.019
Escalante Ortiz, M., & Farrera Pino, R. (2004). Epidemiología del tizón tardío (Phytophthora infestans Mont de Bary) de la papa en zonas productoras del estado Táchira Venezuela. Bioagro, 16(1), 47−54. https://ve.scielo.org/scielo.php?pid=S1316-33612004000100007&script=sci_arttext
Etherton, B. A., Choudhury, R. A., Alcalá Briseño, R. I., Mouafo-Tchinda, R. A., Plex Sulá, A. I., Choudhury, M., Adhikari, A., Lei, S. L., Kraisitudomsook, N., Buritica, J. R., Cerbaro, V. A., Ogero, K., Cox, C. M., Walsh, S. P., Andrade-Piedra, J. L., Omondi, B. A., Navarrete, I., McEwan, M. A., & Garrett, K. A. (2024). Disaster plant pathology: smart solutions for threats to global plant health from natural and human-driven disasters. Phytopathology, 114(5), 855−868. https://doi.org/10.1094/PHYTO-03-24-0079-FI
Fahim, M. A., Medany, M. A., Hadid, A. F. A., Mosa, A. A., & Mostafa, M. H. (2007). Ecological studies and prediction of the onset of potato late blight under Egyptian climatic conditions. Acta Horticulturae, 729, 453−457. https://doi.org/10.17660/ActaHortic.2007.729.76
Fry, W. E., Apple, A. E., & Bruhn, J. A. (1983). Evaluation of potato late blight forecasts modified to incorporate host resistance and fungicide weathering. Phytopathology, 73(7), 1054−1059. https://doi.org/10.1094/Phyto-73-1054
Gardan, L., Gouy, C., Christen, R., & Samson, R. (2003). Elevation of three subspecies of Pectobacterium carotovorum to species level: Pectobacterium atrosepticum sp. nov., Pectobacterium betavasculorum sp. nov. and Pectobacterium wasabiae sp. nov. International Journal of Systematic and Evolutionary Microbiology, 53(2), 381–391. https://doi.org/10.1099/ijs.0.02423-0
Garrett, K. A., Dendy, S. P., Frank, E. E., Rouse, M. N., & Travers, S. E. (2006). Climate change effects on plant disease: genomes to ecosystems. Annual Review of Phytopathology, (44), 489−509. https://doi.org/10.1146/annurev.phyto.44.070505.143420
Garrett, K. A., Madden, L. V., Hughes, G., & Pfender, W. F. (2004). New applications of statistical tools in plant pathology. Phytopathology, 94(9), 999−1003. https://doi.org/10.1094/PHYTO.2004.94.9.999
Garrett, K. A., Nita, M., De Wolf, E. D., Esker, P. D., Gomez- Montano, L., & Sparks, A. H. (2021). Plant pathogens as indicators of climate change. In T. M. Letcher (Ed.), Climate change (3rd ed., pp. 499−513). Elsevier. https://doi.org/10.1016/B978-0-12-821575-3.00024-4
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). CRC Press. https://doi.org/10.1201/b16018
González-Jiménez, J., Andersson, B., Wiik, L., & Zhan, J. (2023). Modelling potato yield losses caused by Phytophthora infestans: Aspects of disease growth rate, infection time and temperature under climate change. Field Crops Research, 299, Article 108977. https://doi.org/10.1016/j.fcr.2023.108977
González-Domínguez, E., Caffi, T., Rossi, V., Salotti, I., & Fedele, G. (2023). Plant disease models and forecasting: changes in principles and applications over the last 50 years. Phytopathology,
113(4), 678−693. https://doi.org/10.1094/PHYTO-10-22-0362-KD
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. https://mitpress.mit.edu/9780262035613/deep-learning/
Greenland, S. (2006). Bayesian perspectives for epidemiological research: I. Foundations and basic methods. International Journal of Epidemiology, 35(3), 765−775. https://doi.org/10.1093/ije/dyi312
Harrison, J. G. (1992). Effects of the aerial environment on late blight of potato foliage: a review. Plant Pathology, 41(4), 384–416. https://doi.org/10.1111/j.1365-3059.1992.tb02435.x
Henderson, D., Williams, C. J., & Miller, J. S. (2007). Forecasting late blight in potato crops of Southern Idaho using logistic regression analysis. Plant Disease, 91(8), 951−956. https://doi.org/10.1094/PDIS-91-8-0951
Hijmans, R. J., Forbes, G. A., & Walker, T. S. (2000). Estimating the global severity of potato late blight with GIS-linked disease forecast models. Plant Pathology, 49(6), 697−705. https://doi.org/10.1046/j.1365-3059.2000.00511.x
Hjelkrem, A.-G. R., Eikemo, H., Le, V. H., Hermansen, A., & Nærstad, R. (2021). A process-based model to forecast risk of potato late blight in Norway (The Nærstad model): model development, sensitivity analysis and Bayesian calibration. Ecological Modelling, 450, Article 109565. https://doi.org/10.1016/j.ecolmodel.2021.109565
Holley, J. D., Hall, R., & Hofstra, G. (1985). Effects of cultivar resistance, leaf wetness duration and temperature on rate of development of potato early blight. Canadian Journal of Plant Science, 65(1), 179−184. https://doi.org/10.4141/cjps85-024
Hrytseva, N. G., & Skivka, L. M. (2023). Latent infestation of potato tubers with soft rot and ring rot pathogens under changing weather conditions in Ukraine. Mikrobiolohichnyi Zhurnal, 85(6), 26−40. https://doi.org/10.15407/microbiolj85.06.026
Hughes, G. (2017). The evidential basis of decision making in plant disease management. Annual Review of Phytopathology, 55, 41−59. https://doi.org/10.1146/annurev-phyto-080516-035342
Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90−95. https://doi.org/10.1109/MCSE.2007.55
Ibrahim, E. S., Nendel, C., Kamali, B., Gajere, E. N., & Hostert, P. (2024). Predicting potato diseases in smallholder agricultural areas of Nigeria using machine learning and remote sensingbased climate data. PhytoFrontiers, 4(2), 89−105. https://doi.org/10.1094/PHYTOFR-10-22-0105-R
Jeger, M. J., & van den Bosch, F. (1993). Threshold criteria for model plant disease epidemics. II. Persistence and endemicity. Phytopathology, 84(1), 28−30. https://www.apsnet.org/publications/phytopathology/backissues/Documents/1994Articles/Phyto84n01_28.PDF
Johnson, D. A., Alldredge, J. R., & Hamm, P. B. (1998). Expansion of potato late blight forecasting models for the Columbia basin of Washington and Oregon. Plant Disease, 82(6), 642−645. https://doi.org/10.1094/PDIS.1998.82.6.642
Johnson, D. A., Alldredge, J. R., & Vakoch, D. L. (1996). Potato late blight forecasting models for the semiarid environment of South-Central Washington. Phytopathology, 86(5), 480−484. https://doi.org/10.1094/Phyto-86-480
Johnson, D. A., Cummings, T. F., Ghanem, R. A., & Alldredge, J. R. (2009). Association of solar irradiance and days of precipitation with incidence of potato late blight in the semiarid environment of the Columbia Basin. Plant Disease, 93(3), 272−280. https://doi.org/10.1094/PDIS-93-3-0272
Khayi, S., Cigna, J., Chong, T. M., Quêtu-Laurent, A., Chan, K.-G., Hélias, V., & Faure, D. (2016). Transfer of the potato plant isolates of Pectobacterium wasabiae to Pectobacterium parmentieri sp. nov. International Journal of Systematic and Evolutionary Microbiology, 66(12), 5379–5383. https://doi.org/10.1099/ijsem.0.001524
Kraus, S., Breier, M., Lim, W. M., Dabić, M., Kumar, S., Kanbach, D., Mukherjee, D., Corvello, V., Piñeiro-Chousa, J., Liguori, E., Palacios-Marqués, D., Schiavone, F., Ferraris, A., Fernandes, C., & Ferreira, J. J. (2022). Literature reviews as independent studies: guidelines for academic practice. Review of Managerial Science, 16(8), 2577−2595. https://doi.org/10.1007/s11846-022-00588-8
Krupnik, T. J., Fernandes, J. M. C., Vargas, F., Ponte, E. M., Hossain, K., Kamal, M., Billah, M., Harun-Or-Rashid., Hussain, Sk. G., Singh, P. K., Roy, K. K., Pizolotto, C. A., Kamal Khan, S., Pavan, W., & Faruq, G. (2025). A weather-forecast driven early warning system for wheat blast disease: User-centered design, validation, and scaling in Bangladesh and Brazil. Climate Services, 39, Article 100589. https://doi.org/10.1016/j.cliser.2025.100589
Lahlali, R., Taoussi, M., Laasli, S.-E., Gachara, G., Ezzouggari, R., Belabess, Z., Aberkani, K., Assouguem, A., Meddich, A., El Jarroudi, M., & Barka, E. A. (2024). Effects of climate change on plant pathogens and host-pathogen interactions. Crop and Environment, 3(3), 159−170. https://doi.org/10.1016/j.crope.2024.05.003
Li, Y., & Chao, X. (2021). Toward sustainability: trade-off between data quality and quantity in crop pest recognition. Frontiers in Plant Science, 12, Article 811241. https://doi.org/10.3389/fpls.2021.811241
Liaqat, N., Khan, M. A., Atiq, M., & Khan, M. A. (2022). Influence of environmental factors on the development of potato blackleg disease. Applied Ecology and Environmental Research, 20(2), 1325−1333. https://doi.org/10.15666/aeer/2002_13251333
Lozada Garcia, B. I., Sentelhas, P. C., Tapia, L. R., & Sparovek, G. (2008). Climatic risk for potato late blight in the Andes region of Venezuela. Scientia Agricola,65, 32−39. https://doi.org/10.1590/S0103-90162008000700007
Madden, L. V. (2025). Reflections on the past, present, and future of quantitative plant disease epidemiology. Annual Review of Phytopathology, 63, 1–22. https://doi.org/10.1146/annurev-phyto-031725-033728
Madden, L. V., & Hughes, G. (1995). Plant disease incidence: distributions, heterogeneity, and temporal analysis. Annual Review of Phytopathology, 33, 529−564. https://doi.org/10.1146/annurev.py.33.090195.002525
Madden, L. V., Hughes, G., & van den Bosch, F. (2017). Introduction. In V. M. Laurence, H. Gareth, & F. van den Bosch (Eds.), The study of plant disease epidemics (pp. 1−10). The American Phytopathological Society. https://doi.org/10.1094/9780890545058.001
MADR. (2021). Cadena de la papa. Dirección de cadenas agrícolas y forestales. Cadena de la papa. https://sioc.minagricultura.gov.co/Papa/Documentos/2021-06-30%20Cifras%20Sectoriales.pdf
Maity, S., & Mandal, P. S. (2022). A comparison of deterministic and stochastic plant-vector-virus models based on probability of disease extinction and outbreak. Bulletin of Mathematical Biology, 84(3), Article 41. https://doi.org/10.1007/s11538-022-01001-x
Meno, L., Abuley, I., Seijo, M. C., & Escuredo, O. (2024). Management strategies for early blight in potatoes: assessment of the TOMCAST model, including the aerobiological risk level and critical phenological period. Agriculture, 14(8), Article 1414. https://doi.org/10.3390/agriculture14081414
Meno, L., Escuredo, O., Abuley, I. K., & Seijo, M. C. (2023). Predicting daily aerobiological risk level of potato late blight using C5.0 and random forest algorithms under field conditions. Sensors, 23(8), Article 3818. https://doi.org/10.3390/s23083818
Meno, L., Escuredo, O., Rodríguez-Flores, M. S., & Seijo, M. C. (2020). Modification of the TOMCAST model with aerobiological data for management of potato early blight. Agronomy, 10(12), Article 1872. https://doi.org/10.3390/agronomy10121872
Michaelides, S. C. (1991). A dynamic model of the interactions between the potato crop and Phytophthora infestans. EPPO Bulletin, 21(3), 515−525. https://doi.org/10.1111/j.1365-2338.1991.tb01283.x
Modesto, O. O., Anwar, M., He, Z., Larkin, R. P., & Honeycutt, C. W. (2016). Survival potential of Phytophthora infestans sporangia in relation to environmental factors and late blight occurrence. Journal of Plant Protection Research, 56(1), 73–81. https://www.plantprotection.pl/pdf-94080-28595?filename=Survivalpotential-of-Phy.pdf
Morita, S., Thall, P. F., & Müller, P. (2010). Evaluating the impact of prior assumptions in Bayesian biostatistics. Statistics in Biosciences, 2(1), 1–17. https://doi.org/10.1007/s12561-010-9018-x
Narouei-Khandan, H. A., Shakya, S. K., Garrett, K. A., Goss, E. M., Dufault, N. S., Andrade-Piedra, J. L., Asseng, S., Wallach, D., & van Bruggen, A. H. C. (2020). BLIGHTSIM: a new potato late blight model simulating the response of phytophthora infestans to diurnal temperature and humidity fluctuations in relation to climate change. Pathogens, 9(8), Article 659. https://doi.org/10.3390/pathogens9080659
Nusrat, I., & Jang, S.-B. (2018). A comparison of regularization techniques in deep neural networks. Symmetry, 10(11), Article 648. https://doi.org/10.3390/sym10110648
Nutter Jr., F. W., van Rij, N., Eggenberger, S. K., & Holah, N. (2010). Spatial and temporal dynamics of plant pathogens. In E.-C. Oerke, R. Gerhards, G. Menz, & R. A. Sikora (Eds.), Precision crop protection − the challenge and use of heterogeneity (pp. 27−50). Springer Dordrecht. https://doi.org/10.1007/978-90-481-9277-9_3
Olanya, O. M., Honeycutt , C. W., Tschöepe ,B., Kleinhenz , B., Lambert , D. H., & Johnson, S. B. (2012). Effectiveness of SIMBLIGHT1 and SIMPHYT1 models for predicting Phytophthora infestans in north-eastern United States. Archives of Phytopathology and Plant Protection, 45(13), 1558−1569. https://doi.org/10.1080/03235408.2012.681502
O’Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter, T. R., Mathur, R., & van Vuuren, D. P. (2014). A new scenario framework for climate change research: The concept of shared socioeconomic pathways. Climatic Change, 122(3), 387–400. https://doi.org/10.1007/s10584-013-0905-2
Pichler, M., & Hartig, F. (2023). Machine learning and deep learning – A review for ecologists. Methods in Ecology and Evolution, 14(4), 994−1016. https://doi.org/10.1111/2041-210X.14061
Rethlefsen, M. L., Kirtley, S., Waffenschmidt, S., Ayala, A. P., Moher, D., Page, M. J., & PRISMA-S Group. (2021). PRISMA-S: an extension to the PRISMA statement for reporting literature searches in systematic reviews. Systematic Reviews, 10(1), Article 39. https://doi.org/10.1186/s13643-020-01542-z
Rodríguez-Almonacid, D. V., Ramírez-Gil, J. G., Higuera, O. L., Hernández, F., & Díaz-Almanza, E. (2023). A comprehensive step-by-step guide to using data science tools in the gestion of epidemiological and climatological data in rice production systems. Agronomy, 13(11), Article 2844. https://doi.org/10.3390/agronomy13112844
Rowlandson, T., Gleason, M., Sentelhas, P., Gillespie, T., Thomas, C., & Hornbuckle, B. (2015). Reconsidering leaf wetness duration determination for plant disease management. Plant Disease, 99(3), 310−319. https://doi.org/10.1094/PDIS-05-14-0529-FE
Savary, S., Willocquet, L., Pethybridge, S. J., Esker, P., McRoberts, N., & Nelson, A. (2019). The global burden of pathogens and pests on major food crops. Nature Ecology & Evolution, 3, 430–439. https://doi.org/10.1038/s41559-018-0793-y
Serinaldi, F. (2024). Scientific logic and spatio-temporal dependence in analyzing extreme-precipitation frequency: negligible or neglected?. Hydrology and Earth System Sciences, 28(14), 3191−3218. https://doi.org/10.5194/hess-28-3191-2024
Shah, D. A., Paul, P. A., De Wolf, E. D., & Madden, L. V. (2019). Predicting plant disease epidemics from functionally represented weather series. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1775), Article 20180273. https://doi.org/10.1098/rstb.2018.0273
Singh, B. K., Delgado-Baquerizo, M., Egidi, E., Guirado, E., Leach, J. E., Liu, H., & Trivedi, P. (2023). Climate change impacts on plant pathogens, food security and paths forward. Nature Reviews Microbiology, 21(10), 640−656. https://doi.org/10.1038/s41579-023-00900-7
Singh, A. K., Ganapathysubramanian, B., Sarkar, S., & Singh, A. (2018). Deep learning for plant stress phenotyping: trends and future perspectives. Trends in Plant Science, 23(10), 883−898. https://doi.org/10.1016/j.tplants.2018.07.004
Skelsey, P., Humphris, S. N., Campbell, E. J., & Toth, I. K. (2018). Threat of establishment of non-indigenous potato blackleg and tuber soft rot pathogens in Great Britain under climate change. PLoS ONE, 13(10), Article e0205711. https://doi.org/10.1371/journal.pone.0205711
Stella, I. R., & Ghosh, M. (2019). Modeling plant disease with biological control of insect pests. Stochastic Analysis and Applications, 37(6), 1133−1154. https://doi.org/10.1080/07362994.2019.1646139
Tebaldi, C., & Knutti, R. (2007). The use of the multi-model ensemble in probabilistic climate projections. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1857), 2053–2075. https://doi.org/10.1098/rsta.2007.2076
Tetko, I. V., van Deursen, R., & Godin, G. (2024). Be aware of overfitting by hyperparameter optimization! Journal of Cheminformatics, 16(1), Article 139. https://doi.org/10.1186/s13321-024-00934-w
Thompson, S., Levin, S., & Rodriguez-Iturbe, I. (2013). Linking plant disease risk and precipitation drivers: a dynamical systems framework. The American Naturalist, 181(1), E1-E16. https://doi.org/10.1086/668572
Tilva, V., Patel, J., & Bhatt, C. (2013). Weather based plant diseases forecasting using fuzzy logic. 2013 Nirma University International Conference on Engineering (NUiCONE), 1−5. https://doi.org/10.1109/NUiCONE.2013.6780173
Trentin, G. (2006). Avaliação de sistemas de previsão de ocorrência de Phytophthora infestans em batata [Master thesis, Universidade Federal de Santa Maria, Brazil]. Repositório Digital da UFSM. https://www.researchgate.net/profile/Gustavo-Trentin/publication/228385114_Avaliacao_de_sistemas_de_previsao_de_ocorrencia_de_Phytophthora_infstans_em_batata/links/0c96052deabb1e90a1000000/Avaliacao-de-sistemas-de-previsao-de-ocorrencia-de-Phytophthora-infestans-em-batata.pdf
Van Dongen, S. (2006). Prior specification in Bayesian statistics: Three cautionary tales. Journal of Theoretical Biology, 242(1), 90–100. https://doi.org/10.1016/j.jtbi.2006.02.002
Van Hall, C. J. J. (1902). Bijdragen tot de Kennis der Bakteriele Plantenziekten. [Inaugural dissertation, University of Amsterdam]. https://doi.org/10.5962/bhl.title.13492
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., & Rose, S. K. (2011). The representative concentration pathways: An overview. Climatic Change, 109(1), Article 5. https://doi.org/10.1007/s10584-011-0148-z
Waskom, M. L. (2021). Seaborn: statistical data visualization. The Journal of Open Source Software, 6(60), Article 3021. https://doi.org/10.21105/joss.03021
Weiss, A. (1990). Leaf wetness: Measurements and models. Remote Sensing Reviews, 5(1), 215−224. https://doi.org/10.1080/02757259009532130
Wilby, R. L., & Wigley, T. M. L. (1997). Downscaling general circulation model output: A review of methods and limitations. Progress in Physical Geography: Earth and Environment, 21(4), 530–548. https://doi.org/10.1177/030913339702100403
Workneh, F., Henne, D. C., Goolsby, J. A., Crosslin, J. M., Whipple, S. D., Bradshaw, J. D., Rashed, A., Paetzold, L., Harveson, R. M., & Rush, C. M. (2013). Characterization of management and environmental factors associated with regional variations in potato zebra chip occurrence. Phytopathology, 103(12), 1235−1242. https://doi.org/10.1094/PHYTO-03-13-0084-R
Yu, Q., Zhang, S., Zhang, H., & Wang, C. (2014). Potato late blight occurrence period forecasting based on polynomial regression analysis. Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering (pp. 539−542). https://doi.org/10.2991/meic-14.2014.121
Yuen, J. E., & Hughes, G. (2002). Bayesian analysis of plant disease prediction. Plant Pathology, 51(4), 407−412. https://doi.org/10.1046/j.0032-0862.2002.00741.x
Dimensions
PlumX
Article abstract page views
Downloads
How to Cite
License
Copyright (c) 2026 Agronomía Colombiana
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
© Centro Editorial de la Facultad de Ciencias Agrarias, Universidad Nacional de Colombia
Reproduction and quotation of material appearing in the journal is authorized provided the following are explicitly indicated: journal name, author(s) name, year, volume, issue and pages of the source. The ideas and observations recorded by the authors are their own and do not necessarily represent the views and policies of the Universidad Nacional de Colombia. Mention of products or commercial firms in the journal does not constitute a recommendation or endorsement on the part of the Universidad Nacional de Colombia; furthermore, the use of such products should comply with the product label recommendations.
The Creative Commons license used by Agronomia Colombiana journal is: Attribution - NonCommercial - ShareAlike (by-nc-sa)
Agronomia Colombiana by Centro Editorial of Facultad de Ciencias Agrarias, Universidad Nacional de Colombia is licensed under a Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional License.
Creado a partir de la obra en http://revistas.unal.edu.co/index.php/agrocol/.
