Geostatistical analysis of bacterial blight in coffee tree seedlings in the nursery

Abstract

In nurseries of coffee tree seedlings (Coffea arabica), there are favorable conditions for bacterial blight epidemics (Pseudomonas syringae pv. garcae). Studying the spatial distribution of diseased plants can help in the adoption of management strategies. Likewise, geostatistics has been applied to shape the spatial distribution and study epidemiological aspects of plant diseases. Thus, this study was developed to characterize the spatial distribution pattern of bacterial blight in a nursery of coffee tree seedlings. The disease progress was monitored over time in 704 seedlings organized in lines and columns in a nursery. Considering the mean diameter of the pots used for producing seedlings, georeferencing was carried out in Cartesian coordinate system for the seedlings in the nursery. The disease incidence data were subjected to non-spatial exploratory analysis and geostatistical analysis. The spherical isotropic semivariogram model was adjusted to the data and data interpolation was performed by ordinary kriging to visualize the spatial distribution of symptomatic seedlings. Bacterial blight epidemic was detected in the nursery during the experimental period, and there was variability and spatial dependence in relation to the distribution of diseased seedlings. As the epidemic progressed, the population of diseased plants increased, as well as the number and the size of the foci and their coalescence. Besides, there was an increase in the range value, sill and nugget effect. The kriging maps showed the disease progress and its variance. The bacterial blight epidemic of coffee tree started with a random spatial distribution pattern, progressing to an aggregate pattern.

Spatial pattern of mottled stripe disease (Herbaspirillum rubrisubalbicans) in sugar cane

“…This is consistent with findings of different spatial patterns among different varieties of sugar cane that have different levels of susceptibility by Belan et al (2018) and in the 2017 season in this study. The differences in spatial patterns between the 2016 and 2017 seasons show the dispersion potential of the disease in this crop system, as well as a latent infestation in the area, which means that the disease may be expressed whenever more susceptible genotypes are planted (6). In addition, the increase in incidence could have been due to means of dispersal such as the sowing or propagation of infected stalks, working with contaminated equipment or tools, or growing more susceptible cultivars or genotypes that are less tolerant to the pathogen.…”

“…Patterns of bacterial infection may also be influenced by host susceptibility (17). This is consistent with findings of different spatial patterns among different varieties of sugar cane that have different levels of susceptibility by Belan et al (2018) and in the 2017 season in this study. The differences in spatial patterns between the 2016 and 2017 seasons show the dispersion potential of the disease in this crop system, as well as a latent infestation in the area, which means that the disease may be expressed whenever more susceptible genotypes are planted (6).…”

“…The spatial patterns expressed by diseases of bacterial origin can be influenced by a variety of factors, including climate, soil characteristics, and progression of the epidemic throughout the zone (22), as well as sampling protocols (6). Patterns of bacterial infection may also be influenced by host susceptibility (17).…”

Variabilidade espaço-temporal da precipitação e efeito sobre a produtividade da soja

“…São numerosos os trabalhos que tentam relacionar a variabilidade produtiva aos caracteres de solo, encontrando normalmente coerências entre as variáveis (CAMBARDELLA et al, 1994;MOTOMIYA et al, 2011), mas jamais explicando completamente a causa das desigualdades espaciais de produção. Outras pesquisas recaem sobre aspectos bióticos (FREITAS et al, 2016;BELAN et al, 2018), também com parcial correlação entre causa-efeito. A grande lacuna científica até aqui está na avaliação da variabilidade espacial e temporal dos elementos meteorológicos dentro da unidade produtiva.…”