Analysis of the Temporal and Spatial Distribution Patterns of Abnormal Vertical Growth in Commercial Macadamia Orchards in Australia

Abnormal vertical growth (AVG) syndrome, which has an unknown aetiology, is a serious threat to the Australian macadamia industry. AVG is characterized by vigorous upright growth and reduced flowering and nut set that results in over 70% yield loss. However, there is a deficiency in knowledge about...

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Main Authors: Mohamed Cassim Mohamed Zakeel, Andrew D W Geering, Olufemi A Akinsanmi
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Proceedings
Subjects:
Online Access:https://www.mdpi.com/2504-3900/36/1/47
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spelling doaj-934a23d499a54115a063bb8b8f6f8ee42020-11-25T01:45:17ZengMDPI AGProceedings2504-39002020-01-013614710.3390/proceedings2019036047proceedings2019036047Analysis of the Temporal and Spatial Distribution Patterns of Abnormal Vertical Growth in Commercial Macadamia Orchards in AustraliaMohamed Cassim Mohamed Zakeel0Andrew D W Geering1Olufemi A Akinsanmi2Centre for Horticultural Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane 4072, AustraliaCentre for Horticultural Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane 4072, AustraliaCentre for Horticultural Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane 4072, AustraliaAbnormal vertical growth (AVG) syndrome, which has an unknown aetiology, is a serious threat to the Australian macadamia industry. AVG is characterized by vigorous upright growth and reduced flowering and nut set that results in over 70% yield loss. However, there is a deficiency in knowledge about the distribution of AVG. In this study, we used spatial analysis to provide insights into the distribution and spread of AVG in commercial macadamia orchards in Australia. Using binary data of AVG occurrence from large-scale surveys of six affected commercial orchards in Queensland (five orchards) and New South Wales (one orchard) in 2012 and 2018, spatio-temporal dynamics of AVG was evaluated. Data were subjected to point-pattern and geostatistical analyses using the R package EPIPHY. The Fisher&#8217;s index of dispersion of all orchards showed aggregated patterns of affected trees in both years, with statistical significance (<i>p </i>&lt; 0.01) of chi-square test. Goodness-of-fit comparisons of incidence data of all orchards with &#946;-binomial distributions showed that AVG incidence increased by 64% over the six-year period. AVG distribution and the &#946;-binomial parameters exhibited strong heterogeneity, which indicates high degree of aggregation and increasing spread of AVG over time. In addition, binary power law and spatial hierarchy tests confirmed the patterns of aggregation in all orchards. These results implicate a biotic agent as the cause of AVG.https://www.mdpi.com/2504-3900/36/1/47proteaceaetree nutspatial dynamicsplant syndromesepidemiological modeling
collection DOAJ
language English
format Article
sources DOAJ
author Mohamed Cassim Mohamed Zakeel
Andrew D W Geering
Olufemi A Akinsanmi
spellingShingle Mohamed Cassim Mohamed Zakeel
Andrew D W Geering
Olufemi A Akinsanmi
Analysis of the Temporal and Spatial Distribution Patterns of Abnormal Vertical Growth in Commercial Macadamia Orchards in Australia
Proceedings
proteaceae
tree nut
spatial dynamics
plant syndromes
epidemiological modeling
author_facet Mohamed Cassim Mohamed Zakeel
Andrew D W Geering
Olufemi A Akinsanmi
author_sort Mohamed Cassim Mohamed Zakeel
title Analysis of the Temporal and Spatial Distribution Patterns of Abnormal Vertical Growth in Commercial Macadamia Orchards in Australia
title_short Analysis of the Temporal and Spatial Distribution Patterns of Abnormal Vertical Growth in Commercial Macadamia Orchards in Australia
title_full Analysis of the Temporal and Spatial Distribution Patterns of Abnormal Vertical Growth in Commercial Macadamia Orchards in Australia
title_fullStr Analysis of the Temporal and Spatial Distribution Patterns of Abnormal Vertical Growth in Commercial Macadamia Orchards in Australia
title_full_unstemmed Analysis of the Temporal and Spatial Distribution Patterns of Abnormal Vertical Growth in Commercial Macadamia Orchards in Australia
title_sort analysis of the temporal and spatial distribution patterns of abnormal vertical growth in commercial macadamia orchards in australia
publisher MDPI AG
series Proceedings
issn 2504-3900
publishDate 2020-01-01
description Abnormal vertical growth (AVG) syndrome, which has an unknown aetiology, is a serious threat to the Australian macadamia industry. AVG is characterized by vigorous upright growth and reduced flowering and nut set that results in over 70% yield loss. However, there is a deficiency in knowledge about the distribution of AVG. In this study, we used spatial analysis to provide insights into the distribution and spread of AVG in commercial macadamia orchards in Australia. Using binary data of AVG occurrence from large-scale surveys of six affected commercial orchards in Queensland (five orchards) and New South Wales (one orchard) in 2012 and 2018, spatio-temporal dynamics of AVG was evaluated. Data were subjected to point-pattern and geostatistical analyses using the R package EPIPHY. The Fisher&#8217;s index of dispersion of all orchards showed aggregated patterns of affected trees in both years, with statistical significance (<i>p </i>&lt; 0.01) of chi-square test. Goodness-of-fit comparisons of incidence data of all orchards with &#946;-binomial distributions showed that AVG incidence increased by 64% over the six-year period. AVG distribution and the &#946;-binomial parameters exhibited strong heterogeneity, which indicates high degree of aggregation and increasing spread of AVG over time. In addition, binary power law and spatial hierarchy tests confirmed the patterns of aggregation in all orchards. These results implicate a biotic agent as the cause of AVG.
topic proteaceae
tree nut
spatial dynamics
plant syndromes
epidemiological modeling
url https://www.mdpi.com/2504-3900/36/1/47
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