Geostatistical modeling of “lethal coconut palm crown atrophy”, a new disease in the State of Pará

Gama, Adriano da Silva; Farias, Paulo Roberto Silva

doi:10.5433/1679-0359.2021v42n6supl2p3603

Cited by 1 publication

(2 citation statements)

References 27 publications

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“…As above, we have two options for analysing the data geostatistically: the earlier technique of separating the trend from the residuals and analysing them separately and the current best procedure by REML. We have done both for comparison; first an ordinary least-squares (OLS) regression, Equation (5), and second REML. The coefficients were as follows:…”

Section: Linear Trendmentioning

confidence: 99%

“…There have been many investigations of the distributions of weeds, plant parasites and crop diseases in the field with attempts to model them statistically and map them with a view to identifying the processes that have brought them about. Recent examples in which the most up-to-date methods of spatial analysis have been applied include bacterial blight in rice [2], virus disease in tomatoes [3], rust in coffee [4], crown atrophy in coconut [5] and weed infestation in cereal crops [6,7]. The most relevant recent example in the context of our investigation is that by Liu et al [8] on microclimatic conditions combined with theoretical disease spread in greenhouses.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Characteristics of the Fungus Powdery Mildew (Erysiphe neolycopersici) on Tomatoes and its Spread in Industrial Greenhouses

Sokolidi,

Webster,

Milne

et al. 2023

Biostatistics Research

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In regions with cool temperate climates, tomatoes are grown on an industrial scale in large greenhouses. There the crops are susceptible to infection by powdery mildew, the fungus Erysiphe neolycopersici, which is introduced largely as fungal spores from outside the greenhouses and spread by wind within them. We have monitored the spread of the disease and mapped its distribution in four commercial greenhouses throughout the growing season to understand its aetiology. We modelled the patterns of infection geostatistically, each comprising a deterministic long-range trend plus a short-range spatially correlated random residual. We identified three main kinds of pattern; one consisted of a constant plus a spatially correlated residual, second comprised a linear trend throughout the greenhouse plus a correlated random residual, and third, the trend had the form of a bell akin to a Gaussian surface plus, again, a correlated random residual. Here, we show three examples of these distributions and the detail of their geostatistical analysis using both the traditional method of moments (MoM) estimation of variograms and residual maximum likelihood (REML) to separate the deterministic and random components. The analytical modelling is followed by ordinary punctual kriging in the first case, by universal kriging in the second, and by regression kriging in the third case to display the infection as isarithmic ("contour") maps. We interpret the first form of distribution as arising from numerous foci as spores landed on the leaves from various sources spread by air currents and the movement of workers along the paths through the greenhouse. In the second case, the disease seemed to have spread from an infection introduced through the main door in one corner of the greenhouse and spread from there by the workers and air currents. The third infection arose near the centre of the greenhouse by the main path and spread outwards from there. In all three examples, the main pathways seemed important routes along which the fungus spread.

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Section: Linear Trendmentioning

confidence: 99%