Banana production is affected by Yellow Sigatoka, one of the causes of leaf lesions, which causes the reduction of the photosynthetic area of the plant and, consequently, the quality of the fruit and the production. The objective of this study was to analyze using geostatistics and comparing separable and non-separable spatio-temporal covariance models with different adjustment methods. The experiment was carried out in a banana plantation of the Prata-Anã variety, which presented high severity of the disease, without any control measures, which allowed the study of behavior under natural conditions. The Separable Doubly Exponential and the non-separable model of Gneiting were tested with the Weight Least Squares (WLS), Restricted Maximum Likelihood (REML) and Likelihood Pairwise estimation methods. The Gneiting model, WLS curve-fitting methods for estimatives space-time covariance structure, allowed to reduce the uncertainties of the spatial and temporal prediction of the disease, as well as to characterize the spatio-temporal pattern of the disease.
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The National Council for Scientific and Technological Development (CNPq) provided the Master’s scholarship in Engineering Department in Federal University of Lavras, to the site Vale dos Ventos for allowing the use of the experimental area and to the Foundation for Research Support of the State of Minas Gerais (FAPEMIG), for financing the project.
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Rodrigues, J.D.P., Alves, M.C., Freitas, A.S. et al. Geostatistical stationary space-time covariance functions modeling of Yellow Sigatoka progress in banana crop. Australasian Plant Pathol. 48, 233–244 (2019). https://doi.org/10.1007/s13313-019-0622-z