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Computers and Electronics in Agriculture
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Article . 2016
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Computers and Electronics in Agriculture
Article . 2016 . Peer-reviewed
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Article . 2016
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Delineation of site-specific management units for operational applications using the topographic position index in La Pampa, Argentina

Authors: María Soledad Mieza; Walter Rubén Cravero; Federico Darío Kovac; Pablo Gastón Bargiano;

Delineation of site-specific management units for operational applications using the topographic position index in La Pampa, Argentina

Abstract

Display Omitted Yield values are related to water availability, soil texture and topography.The topographic position index (TPI) can be used to evaluate local topography.TPI correlates better with yield than topography.The topographic features were characterized using the autocorrelation function.Classified TPI maps can be used to delineate MU for operational applications. In this work we propose the use of the topographic position index (TPI), which takes into account the local topography for a given neighborhood, to delineate management units (MU) for site-specific systems. This study was performed in the province of La Pampa, in central Argentina, an area with sandy soils where the main limiting condition for crops is soil moisture. Usually, multi-annual yield maps are used for the delineation of MU. However, those are strongly influenced by issues that could be related to un-calibrated data and previous agronomical practices. Thus, there was a need for a methodology based on stable and unbiased parameters. The methodology was developed for a representative 100ha field. The average size and orientation of the topographic structures were characterized applying the autocorrelation function on the topographic data, which was then used to determine an optimum neighborhood size for the TPI. TPI performed better than the topographic map to characterize the variability of the field. The correlation between yield and TPI was higher (r=0.74) than that between yield and topography (r=0.54). The resulting management units were delineated using an unsupervised classification approach on the TPI maps. From the confusion matrices, the overall accuracy was higher for the TPI derived maps than for the topography derived maps (62% against 47%) when compared to a yield map used as reference. We estimate that this methodology could be used for operational applications, the only requirement being topographic data for a given field, since it is simple, the algorithms used are unbiased and it could be performed using free software.

Country
Argentina
Keywords

Precision Farming, Argentina, Topographic Position Index (Tpi), Autocorrelation Function, Management Unit, Site Specific Management, https://purl.org/becyt/ford/1.5, https://purl.org/becyt/ford/1

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
22
Top 10%
Top 10%
Average
Green
hybrid