2020
DOI: 10.17660/ejhs.2020/85.4.1
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Innovative approaches to orchard management: assessing the variability in yield and maturity in a ‘Gala’ apple orchard using a simple management unit modeling approach

Abstract: Stratification of spatial data into management classes is a common way of interpreting and managing spatial agricultural data. High-resolution environmental and crop production information was collected within a 2.2-ha apple orchard (Malus domestica cv. Gala) near Sydney, NSW, Australia. Classifying the block into management units using the environmental data did not help to interpret the observed apple spatial variation in apple production. A backwards modelling approach was subsequently undertaken, effective… Show more

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Cited by 14 publications
(6 citation statements)
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“…Soil indices derived by proximal sensing (ECa 1 , ECa 2 , and TC_gamma) were less able to predict fruit yield (r = 0.18, 0.29 and 0.01, respectively) and TCSA increment (0.02, 0.15 and 0.01) when all irrigation treatments were considered together. A wide range of correlation coefficients between ECa and fruit yield (R 2 comprised between 0.01 and 0.94) was reported in previous studies carried out on different cultivars of apple trees [44,66]. In a previous study carried out in a commercial olive orchard, significant relationships between the soil characteristics (organic matter, B and Ca) derived by a systematic sampling grid and the fruit yield were measured only in one of the two experimental years [67].…”
Section: Comparing Proximal and Remote Sensing Indices Against Tree Performancementioning
confidence: 72%
“…Soil indices derived by proximal sensing (ECa 1 , ECa 2 , and TC_gamma) were less able to predict fruit yield (r = 0.18, 0.29 and 0.01, respectively) and TCSA increment (0.02, 0.15 and 0.01) when all irrigation treatments were considered together. A wide range of correlation coefficients between ECa and fruit yield (R 2 comprised between 0.01 and 0.94) was reported in previous studies carried out on different cultivars of apple trees [44,66]. In a previous study carried out in a commercial olive orchard, significant relationships between the soil characteristics (organic matter, B and Ca) derived by a systematic sampling grid and the fruit yield were measured only in one of the two experimental years [67].…”
Section: Comparing Proximal and Remote Sensing Indices Against Tree Performancementioning
confidence: 72%
“…In a Mediterranean peach orchard, these gains can include increased production and also increased sustainability with higher level of soil organic carbon and litter carbon pools [23]. However, to date, orchard soil and water management often rely only on grower and extension service experience or, in the most advanced cases, are driven by data on soil water content and/or climate conditions and plant status [24][25][26]. Real-time tree performance, as well as the inter-relation among the different chemical, physical and microbiological variables affecting soil fertility, is little considered.…”
Section: Introductionmentioning
confidence: 99%
“…Spatial variability in the trunk cross-sectional area, yield (Manfrini et al, 2020), fruit quality, canopy LA (Tsoulias et al, 2022), and the flower set (Penzel et al, 2021c) of individual fruit trees in commercial orchards have been described previously. Among the endogenous growth factors, it may be shown that both the small and large scale spatial variability of soil properties (Umali et al, 2012;Käthner et al, 2017) and terrain features can contribute to the inter-tree variability in yield and fruit quality.…”
Section: Introductionmentioning
confidence: 99%