A weighted multivariate spatial clustering model to determine irrigation management zones

A weighted multivariate spatial clustering model to determine irrigation management zones

Trabajo desarrollado bajo la financiación del proyecto “Soil Hydrology research platform underpinning innovation to manage water scarcity in European and Chinese cropping Systems” (773903), coordinado por José Alfonso Gómez Calero, investigador del Instituto de Agricultura Sostenible (IAS)

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Bibliographic Details
Main Authors: Ohana-Levi, Noa, Bahat, Idan, Peeters, Aviva, Shtein, Alexandra, Netzer, Yishai, Cohen, Yafit, Ben-Gal, Alon
Other Authors: Ministry of Agriculture & Rural Development (Israel)
Format: artículo biblioteca
Language:English
Published: Elsevier 2019-07
Subjects:Precision irrigation, Machine learning, Spatial modeling,
Online Access:http://hdl.handle.net/10261/221520
http://dx.doi.org/10.13039/501100000780
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spelling dig-ias-es-10261-2215202020-10-26T07:26:31Z A weighted multivariate spatial clustering model to determine irrigation management zones Ohana-Levi, Noa Bahat, Idan Peeters, Aviva Shtein, Alexandra Netzer, Yishai Cohen, Yafit Ben-Gal, Alon Ministry of Agriculture & Rural Development (Israel) European Commission Precision irrigation Machine learning Spatial modeling Trabajo desarrollado bajo la financiación del proyecto “Soil Hydrology research platform underpinning innovation to manage water scarcity in European and Chinese cropping Systems” (773903), coordinado por José Alfonso Gómez Calero, investigador del Instituto de Agricultura Sostenible (IAS) Management of agricultural fields according to spatial and temporal variability is an important aspect of precision agriculture. Precision management relies on division of a field into areas with homogeneous characteristics, management zones (MZs), which are likely affected by multiple, interrelated factors. We present a method, based on machine learning and spatial statistics, to analyze the spatial relationship between a set of variables and determine management zones in a vineyard. The method involves: (1) fitting a model that quantifies the relationship between multiple variables and yield; (2) fitting a model that quantifies the effect of the spatial variability of multiple variables on yield spatial characteristics; and (3) developing a weighted multivariate spatial clustering model as a method to determine MZs. Twelve variables were sampled for 3893 vines in the wine grape vineyard. These variables included soil properties, terrain characteristics, and environmental impact, as well as crop-condition, using indices calculated from remote sensing images. The predictor variables were spatially characterized using hot-spot analysis (Getis Ord Gi* Z-score values) to assess their spatial variability. A gradient boosted regression trees (BRT) algorithm was used to analyze the spatial multivariable effect on yield spatial characteristics. MZs were determined using multivariate K-means clustering, with relative weights given to the predictors, based on their relative influence on yield spatial variability provided by the BRT model. This method was compared to ordinary K-means clustering and K-means with spatial representation of the variables without weights using a dissimilarity index and spatial autocorrelation measures. Model performance was found to be very high and demonstrated that among the evaluated predictors, crop condition indices were the most important regressors for yield and its spatial characteristics. The weighted multivariate spatial clustering was found to perform better in terms of separability of the points and their spatial distribution than the other two clustering techniques. Quantifying yield and its within-field spatial variability, ranking the effects of the predictors and their spatial variabilities, and segmentation of MZs through multivariable spatial analysis, are expected to benefit irrigation management and agricultural decision-making processes. This research is a part of The “Eugene Kendel” Project for Development of Precision Drip Irrigation funded via the Ministry of Agriculture and Rural Development in Israel (Grant No. 20-12-0030). This project has also received funding from the European Union’s Horizon 2020 research and innovation programme under Project SHui, grant agreement No 773903. Peer reviewed 2020-10-21T12:05:54Z 2020-10-21T12:05:54Z 2019-07 artículo http://purl.org/coar/resource_type/c_6501 Computers and Electronics in Agriculture 162: 719-731 (2019) 0168-1699 http://hdl.handle.net/10261/221520 10.1016/j.compag.2019.05.012 http://dx.doi.org/10.13039/501100000780 en #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/EC/H2020/773903 Publisher's version https://doi.org/10.1016/j.compag.2019.05.012 No open Elsevier
institution IAS ES
collection DSpace
country España
countrycode ES
component Bibliográfico
access En linea
databasecode dig-ias-es
tag biblioteca
region Europa del Sur
libraryname Biblioteca del IAS España
language English
topic Precision irrigation
Machine learning
Spatial modeling
Precision irrigation
Machine learning
Spatial modeling
spellingShingle Precision irrigation
Machine learning
Spatial modeling
Precision irrigation
Machine learning
Spatial modeling
Ohana-Levi, Noa
Bahat, Idan
Peeters, Aviva
Shtein, Alexandra
Netzer, Yishai
Cohen, Yafit
Ben-Gal, Alon
A weighted multivariate spatial clustering model to determine irrigation management zones
description Trabajo desarrollado bajo la financiación del proyecto “Soil Hydrology research platform underpinning innovation to manage water scarcity in European and Chinese cropping Systems” (773903), coordinado por José Alfonso Gómez Calero, investigador del Instituto de Agricultura Sostenible (IAS)
author2 Ministry of Agriculture & Rural Development (Israel)
author_facet Ministry of Agriculture & Rural Development (Israel)
Ohana-Levi, Noa
Bahat, Idan
Peeters, Aviva
Shtein, Alexandra
Netzer, Yishai
Cohen, Yafit
Ben-Gal, Alon
format artículo
topic_facet Precision irrigation
Machine learning
Spatial modeling
author Ohana-Levi, Noa
Bahat, Idan
Peeters, Aviva
Shtein, Alexandra
Netzer, Yishai
Cohen, Yafit
Ben-Gal, Alon
author_sort Ohana-Levi, Noa
title A weighted multivariate spatial clustering model to determine irrigation management zones
title_short A weighted multivariate spatial clustering model to determine irrigation management zones
title_full A weighted multivariate spatial clustering model to determine irrigation management zones
title_fullStr A weighted multivariate spatial clustering model to determine irrigation management zones
title_full_unstemmed A weighted multivariate spatial clustering model to determine irrigation management zones
title_sort weighted multivariate spatial clustering model to determine irrigation management zones
publisher Elsevier
publishDate 2019-07
url http://hdl.handle.net/10261/221520
http://dx.doi.org/10.13039/501100000780
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