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dc.contributor.author
Scudiero, Elia
dc.contributor.author
Teatini, Pietro
dc.contributor.author
Manoli, Gabriele
dc.contributor.author
Braga, Federica
dc.contributor.author
Skaggs, Todd H.
dc.contributor.author
Morari, Francesco
dc.date.accessioned
2018-11-30T17:25:28Z
dc.date.available
2018-11-17T05:36:38Z
dc.date.available
2018-11-30T17:25:28Z
dc.date.issued
2018-11-07
dc.identifier.issn
2073-4395
dc.identifier.other
10.3390/agronomy8110253
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/304434
dc.identifier.doi
10.3929/ethz-b-000304434
dc.description.abstract
Management zones (MZs) are used in precision agriculture to diversify agronomic management across a field. According to current common practices, MZs are often spatially static: they are developed once and used thereafter. However, the soil–plant relationship often varies over time and space, decreasing the efficiency of static MZ designs. Therefore, we propose a novel workflow for time-specific MZ delineation based on integration of plant and soil sensing data. The workflow includes four steps: (1) geospatial sensor measurements are used to describe soil spatial variability and in-season plant growth status; (2) moving-window regression modelling is used to characterize the sub-field changes of the soil–plant relationship; (3) soil information and sub-field indicator(s) of the soil–plant relationship (i.e., the local regression slope coefficient[s]) are used to delineate time-specific MZs using fuzzy cluster analysis; and (4) MZ delineation is evaluated and interpreted. We illustrate the workflow with an idealized, yet realistic, example using synthetic data and with an experimental example from a 21-ha maize field in Italy using two years of maize growth, soil apparent electrical conductivity and normalized difference vegetation index (NDVI) data. In both examples, the MZs were characterized by unique combinations of soil properties and soil–plant relationships. The proposed approach provides an opportunity to address the spatiotemporal nature of changes in crop genetics × environment × management interactions.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
MDPI
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
remote sensing
en_US
dc.subject
proximal sensing
en_US
dc.subject
crop modeling
en_US
dc.subject
soil
en_US
dc.subject
plant
en_US
dc.subject
management zone
en_US
dc.subject
spatial variability
en_US
dc.subject
temporal variability
en_US
dc.subject
precision agriculture
en_US
dc.title
Workflow to establish time-specific zones in precision agriculture by spatiotemporal integration of plant and soil sensing data
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Agronomy
ethz.journal.volume
8
en_US
ethz.journal.issue
11
en_US
ethz.pages.start
253
en_US
ethz.size
21 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Basel
en_US
ethz.publication.status
published
ethz.date.deposited
2018-11-17T05:36:41Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-11-30T17:26:08Z
ethz.rosetta.lastUpdated
2023-02-06T16:39:15Z
ethz.rosetta.versionExported
true
ethz.COinS
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