Zobrazit v EDS

Performances of Machine Learning Algorithms in Predicting the Productivity of Conservation Agriculture at a Global Scale

Uloženo v:
Podrobná bibliografie
Název: Performances of Machine Learning Algorithms in Predicting the Productivity of Conservation Agriculture at a Global Scale
Autoři: Su, Yang, Zhang, Huang, 1993, Gabrielle, Benoit, Makowski, David
Zdroj: Frontiers in Environmental Science. 10
Témata: conservation agriculture, Algorithm comparison, model performance, crop yield, machine learning
Popis: Assessing the productive performance of conservation agriculture (CA) has become a major issue due to growing concerns about global food security and sustainability. Numerous experiments have been conducted to assess the performance of CA under various local conditions, and meta-analysis has become a standard approach in agricultural sector for analysing and summarizing the experimental data. Meta-analysis provides valuable synthetic information based on mean effect size estimation. However, summarizing large amounts of information by way of a single mean effect value is not always satisfactory, especially when considering agricultural practices. Indeed, their impacts on crop yields are often non-linear, and vary widely depending on a number of factors, including soil properties and local climate conditions. To address this issue, here we present a machine learning approach to produce data-driven global maps describing the spatial distribution of the productivity of CA versus conventional tillage (CT). Our objective is to evaluate and compare several machine-learning models for their ability in estimating the productivity of CA systems, and to analyse uncertainty in the model outputs. We consider different usages, including classification, point regression and quantile regression. Our approach covers the comparison of 12 different machine learning algorithms, model training, tuning with cross-validation, testing, and global projection of results. The performances of these algorithms are compared based on a recent global dataset including more than 4,000 pairs of crop yield data for CA vs. CT. We show that random forest has the best performance in classification and regression, while quantile regression forest performs better than quantile neural networks in quantile regression. The best algorithms are used to map crop productivity of CA vs. CT at the global scale, and results reveal that the performance of CA vs. CT is characterized by a strong spatial variability, and that the probability of yield gain with CA is highly dependent on geographical locations. This result demonstrates that our approach is much more informative than simply presenting average effect sizes produced by standard meta-analyses, and paves the way for such probabilistic, spatially-explicit approaches in many other fields of research.
Popis souboru: electronic
Přístupová URL adresa: https://research.chalmers.se/publication/528873
https://research.chalmers.se/publication/528873/file/528873_Fulltext.pdf
Databáze: SwePub
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://research.chalmers.se/publication/528873#
    Name: EDS - SwePub (s4221598)
    Category: fullText
    Text: View record in SwePub
  – Url: https://resolver.ebscohost.com/openurl?sid=EBSCO:edsswe&genre=article&issn=2296665X&ISBN=&volume=10&issue=&date=20220101&spage=&pages=&title=Frontiers in Environmental Science&atitle=Performances%20of%20Machine%20Learning%20Algorithms%20in%20Predicting%20the%20Productivity%20of%20Conservation%20Agriculture%20at%20a%20Global%20Scale&aulast=Su%2C%20Yang&id=DOI:10.3389/fenvs.2022.812648
    Name: Full Text Finder
    Category: fullText
    Text: Full Text Finder
    Icon: https://imageserver.ebscohost.com/branding/images/FTF.gif
    MouseOverText: Full Text Finder
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Su%20Y
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edsswe
DbLabel: SwePub
An: edsswe.oai.research.chalmers.se.e9b23405.126f.45b5.a3e8.b5671d6a5498
RelevancyScore: 961
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 961.017700195313
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Performances of Machine Learning Algorithms in Predicting the Productivity of Conservation Agriculture at a Global Scale
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Su%2C+Yang%22">Su, Yang</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Huang%22">Zhang, Huang</searchLink>, 1993<br /><searchLink fieldCode="AR" term="%22Gabrielle%2C+Benoit%22">Gabrielle, Benoit</searchLink><br /><searchLink fieldCode="AR" term="%22Makowski%2C+David%22">Makowski, David</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <i>Frontiers in Environmental Science</i>. 10
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22conservation+agriculture%22">conservation agriculture</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithm+comparison%22">Algorithm comparison</searchLink><br /><searchLink fieldCode="DE" term="%22model+performance%22">model performance</searchLink><br /><searchLink fieldCode="DE" term="%22crop+yield%22">crop yield</searchLink><br /><searchLink fieldCode="DE" term="%22machine+learning%22">machine learning</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Assessing the productive performance of conservation agriculture (CA) has become a major issue due to growing concerns about global food security and sustainability. Numerous experiments have been conducted to assess the performance of CA under various local conditions, and meta-analysis has become a standard approach in agricultural sector for analysing and summarizing the experimental data. Meta-analysis provides valuable synthetic information based on mean effect size estimation. However, summarizing large amounts of information by way of a single mean effect value is not always satisfactory, especially when considering agricultural practices. Indeed, their impacts on crop yields are often non-linear, and vary widely depending on a number of factors, including soil properties and local climate conditions. To address this issue, here we present a machine learning approach to produce data-driven global maps describing the spatial distribution of the productivity of CA versus conventional tillage (CT). Our objective is to evaluate and compare several machine-learning models for their ability in estimating the productivity of CA systems, and to analyse uncertainty in the model outputs. We consider different usages, including classification, point regression and quantile regression. Our approach covers the comparison of 12 different machine learning algorithms, model training, tuning with cross-validation, testing, and global projection of results. The performances of these algorithms are compared based on a recent global dataset including more than 4,000 pairs of crop yield data for CA vs. CT. We show that random forest has the best performance in classification and regression, while quantile regression forest performs better than quantile neural networks in quantile regression. The best algorithms are used to map crop productivity of CA vs. CT at the global scale, and results reveal that the performance of CA vs. CT is characterized by a strong spatial variability, and that the probability of yield gain with CA is highly dependent on geographical locations. This result demonstrates that our approach is much more informative than simply presenting average effect sizes produced by standard meta-analyses, and paves the way for such probabilistic, spatially-explicit approaches in many other fields of research.
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: electronic
– Name: URL
  Label: Access URL
  Group: URL
  Data: <link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/528873" linkWindow="_blank">https://research.chalmers.se/publication/528873</link><br /><link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/528873/file/528873_Fulltext.pdf" linkWindow="_blank">https://research.chalmers.se/publication/528873/file/528873_Fulltext.pdf</link>
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsswe&AN=edsswe.oai.research.chalmers.se.e9b23405.126f.45b5.a3e8.b5671d6a5498
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3389/fenvs.2022.812648
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: conservation agriculture
        Type: general
      – SubjectFull: Algorithm comparison
        Type: general
      – SubjectFull: model performance
        Type: general
      – SubjectFull: crop yield
        Type: general
      – SubjectFull: machine learning
        Type: general
    Titles:
      – TitleFull: Performances of Machine Learning Algorithms in Predicting the Productivity of Conservation Agriculture at a Global Scale
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Su, Yang
      – PersonEntity:
          Name:
            NameFull: Zhang, Huang
      – PersonEntity:
          Name:
            NameFull: Gabrielle, Benoit
      – PersonEntity:
          Name:
            NameFull: Makowski, David
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 2296665X
            – Type: issn-locals
              Value: SWEPUB_FREE
            – Type: issn-locals
              Value: CTH_SWEPUB
          Numbering:
            – Type: volume
              Value: 10
          Titles:
            – TitleFull: Frontiers in Environmental Science
              Type: main
ResultId 1