Publicação
Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach
| Resumo: | Climate change is affecting global viticulture, increasing heatwaves and drought. Precision irrigation, supported by robust water status indicators (WSIs), is inevitable in most of the Mediterranean basin. One of the most reliable WSIs is the leaf water potential ((Formula presented.)), which is determined via an intrusive and time-consuming method. The aim of this work is to discern the most effective variables that are correlated with plants’ water status and identify the variables that better predict (Formula presented.). Five grapevine varieties grown in the Alentejo region (Portugal) were selected and subjected to three irrigation treatments, starting in 2018: full irrigation (FI), deficit irrigation (DI), and no irrigation (NI). Plant monitoring was performed in 2023. Measurements included stomatal conductance ((Formula presented.)), predawn water potential (Formula presented.), stem water potential ((Formula presented.)), thermal imaging, and meteorological data. The WSIs, namely (Formula presented.) and (Formula presented.), responded differently according to the irrigation treatment. (Formula presented.) measured at mid-morning (MM) and mid-day (MD) proved unable to discern between treatments. MM measurements presented the best correlations between WSIs. (Formula presented.) showed the best correlations between the other WSIs, and consequently the best predictive capability to estimate (Formula presented.). Machine learning regression models were trained on meteorological, thermal, and (Formula presented.) data to predict (Formula presented.), with ensemble models showing a great performance (ExtraTrees: (Formula presented.), (Formula presented.) ; Gradient Boosting: (Formula presented.) ; (Formula presented.)). |
|---|---|
| Autores principais: | Damásio, Miguel |
| Outros Autores: | Barbosa, Miguel; Deus, João; Fernandes, Eduardo; Leitão, André; Albino, Luís; Fonseca, Filipe; Silvestre, José |
| Assunto: | modelling precision irrigation predawn leaf water potential Vitis vinifera water status indicators Ecology, Evolution, Behavior and Systematics Ecology Plant Science SDG 13 - Climate Action |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1868983039493668864 |
|---|---|
| author | Damásio, Miguel |
| author2 | Barbosa, Miguel Deus, João Fernandes, Eduardo Leitão, André Albino, Luís Fonseca, Filipe Silvestre, José |
| author2_role | author author author author author author author |
| author_facet | Damásio, Miguel Barbosa, Miguel Deus, João Fernandes, Eduardo Leitão, André Albino, Luís Fonseca, Filipe Silvestre, José |
| author_role | author |
| contributor_name_str_mv | Bioresources 4 Sustainability (GREEN-IT) MDPI - Multidisciplinary Digital Publishing Institute RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Damásio, Miguel\"},{\"Person.name\":\"Barbosa, Miguel\"},{\"Person.name\":\"Deus, João\"},{\"Person.name\":\"Fernandes, Eduardo\"},{\"Person.name\":\"Leitão, André\"},{\"Person.name\":\"Albino, Luís\"},{\"Person.name\":\"Fonseca, Filipe\"},{\"Person.name\":\"Silvestre, José\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Bioresources 4 Sustainability (GREEN-IT) MDPI - Multidisciplinary Digital Publishing Institute RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Damásio, Miguel Barbosa, Miguel Deus, João Fernandes, Eduardo Leitão, André Albino, Luís Fonseca, Filipe Silvestre, José |
| datacite.date.Accepted.fl_str_mv | 2023-12-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2024-04-02T23:46:48Z |
| datacite.date.embargoed.fl_str_mv | 2024-04-02T23:46:48Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | modelling precision irrigation predawn leaf water potential Vitis vinifera water status indicators Ecology, Evolution, Behavior and Systematics Ecology Plant Science SDG 13 - Climate Action |
| datacite.titles.title.fl_str_mv | Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach |
| dc.contributor.none.fl_str_mv | Bioresources 4 Sustainability (GREEN-IT) MDPI - Multidisciplinary Digital Publishing Institute RUN |
| dc.creator.none.fl_str_mv | Damásio, Miguel Barbosa, Miguel Deus, João Fernandes, Eduardo Leitão, André Albino, Luís Fonseca, Filipe Silvestre, José |
| dc.date.Accepted.fl_str_mv | 2023-12-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2024-04-02T23:46:48Z |
| dc.date.embargoed.fl_str_mv | 2024-04-02T23:46:48Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/165723 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | modelling precision irrigation predawn leaf water potential Vitis vinifera water status indicators Ecology, Evolution, Behavior and Systematics Ecology Plant Science SDG 13 - Climate Action |
| dc.title.fl_str_mv | Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Climate change is affecting global viticulture, increasing heatwaves and drought. Precision irrigation, supported by robust water status indicators (WSIs), is inevitable in most of the Mediterranean basin. One of the most reliable WSIs is the leaf water potential ((Formula presented.)), which is determined via an intrusive and time-consuming method. The aim of this work is to discern the most effective variables that are correlated with plants’ water status and identify the variables that better predict (Formula presented.). Five grapevine varieties grown in the Alentejo region (Portugal) were selected and subjected to three irrigation treatments, starting in 2018: full irrigation (FI), deficit irrigation (DI), and no irrigation (NI). Plant monitoring was performed in 2023. Measurements included stomatal conductance ((Formula presented.)), predawn water potential (Formula presented.), stem water potential ((Formula presented.)), thermal imaging, and meteorological data. The WSIs, namely (Formula presented.) and (Formula presented.), responded differently according to the irrigation treatment. (Formula presented.) measured at mid-morning (MM) and mid-day (MD) proved unable to discern between treatments. MM measurements presented the best correlations between WSIs. (Formula presented.) showed the best correlations between the other WSIs, and consequently the best predictive capability to estimate (Formula presented.). Machine learning regression models were trained on meteorological, thermal, and (Formula presented.) data to predict (Formula presented.), with ensemble models showing a great performance (ExtraTrees: (Formula presented.), (Formula presented.) ; Gradient Boosting: (Formula presented.) ; (Formula presented.)). |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/1e25b75c-11d6-4ab4-8b4a-44fbe9296f03/download |
| id | run_b67af07401bbefa22292a67d8940ac21 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/165723 |
| inst_facet_str | urn:organizationAcronym:unl{{{_:::_}}}Universidade Nova de Lisboa |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/165723 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Damásio, Miguel Barbosa, Miguel Deus, João Fernandes, Eduardo Leitão, André Albino, Luís Fonseca, Filipe Silvestre, José |
| publishDate | 2023 |
| repo_facet_str | urn:repositoryAcronym:run{{{_:::_}}}Repositório Institucional da UNL |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engenClimate change is affecting global viticulture, increasing heatwaves and drought. Precision irrigation, supported by robust water status indicators (WSIs), is inevitable in most of the Mediterranean basin. One of the most reliable WSIs is the leaf water potential ((Formula presented.)), which is determined via an intrusive and time-consuming method. The aim of this work is to discern the most effective variables that are correlated with plants’ water status and identify the variables that better predict (Formula presented.). Five grapevine varieties grown in the Alentejo region (Portugal) were selected and subjected to three irrigation treatments, starting in 2018: full irrigation (FI), deficit irrigation (DI), and no irrigation (NI). Plant monitoring was performed in 2023. Measurements included stomatal conductance ((Formula presented.)), predawn water potential (Formula presented.), stem water potential ((Formula presented.)), thermal imaging, and meteorological data. The WSIs, namely (Formula presented.) and (Formula presented.), responded differently according to the irrigation treatment. (Formula presented.) measured at mid-morning (MM) and mid-day (MD) proved unable to discern between treatments. MM measurements presented the best correlations between WSIs. (Formula presented.) showed the best correlations between the other WSIs, and consequently the best predictive capability to estimate (Formula presented.). Machine learning regression models were trained on meteorological, thermal, and (Formula presented.) data to predict (Formula presented.), with ensemble models showing a great performance (ExtraTrees: (Formula presented.), (Formula presented.) ; Gradient Boosting: (Formula presented.) ; (Formula presented.)).application/pdfenCan Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning ApproachDamásio, MiguelBarbosa, MiguelDeus, JoãoFernandes, EduardoLeitão, AndréAlbino, LuísFonseca, FilipeSilvestre, JoséBioresources 4 Sustainability (GREEN-IT)MDPI - Multidisciplinary Digital Publishing InstituteHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptISSNIsPartOf2223-7747URNIsPartOfPURE: 83537371URNIsPartOfPURE UUID: 4837d3f1-fe2e-45e8-b166-9a4018927200URNIsPartOfScopus: 85180671980DOIIsPartOf10.3390/plants122441422024-04-02T23:46:48Z2023-122023-12-01T00:00:00ZHandlehttp://hdl.handle.net/10362/165723http://purl.org/coar/access_right/c_abf2open accessmodellingprecision irrigationpredawn leaf water potentialVitis viniferawater status indicatorsEcology, Evolution, Behavior and SystematicsEcologyPlant ScienceSDG 13 - Climate Action2789023 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/1e25b75c-11d6-4ab4-8b4a-44fbe9296f03/download |
| spellingShingle | Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach Damásio, Miguel modelling precision irrigation predawn leaf water potential Vitis vinifera water status indicators Ecology, Evolution, Behavior and Systematics Ecology Plant Science SDG 13 - Climate Action |
| status | SINGLETON |
| subject.fl_str_mv | modelling precision irrigation predawn leaf water potential Vitis vinifera water status indicators Ecology, Evolution, Behavior and Systematics Ecology Plant Science SDG 13 - Climate Action |
| title | Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach |
| title_full | Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach |
| title_fullStr | Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach |
| title_full_unstemmed | Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach |
| title_short | Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach |
| title_sort | Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach |
| topic | modelling precision irrigation predawn leaf water potential Vitis vinifera water status indicators Ecology, Evolution, Behavior and Systematics Ecology Plant Science SDG 13 - Climate Action |
| topic_facet | modelling precision irrigation predawn leaf water potential Vitis vinifera water status indicators Ecology, Evolution, Behavior and Systematics Ecology Plant Science SDG 13 - Climate Action |
| url | http://hdl.handle.net/10362/165723 |
| visible | 1 |