Loading…
Relative influence of climate and agroenvironmental factors on wireworm damage risk in maize crops
A large-scale survey was carried out in 336 French fields to investigate the influence of soil characteristics, climate conditions, the presence of wireworms and the identity of predominant species, agricultural practices, field history and local landscape features on the damage caused by wireworms...
Saved in:
Published in: | Journal of pest science 2018-03, Vol.91 (2), p.585-599 |
---|---|
Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c421t-a13cf6c3c07cf710ced558599dd8e44935c990a66d4ab89bf629231ab453e4b23 |
---|---|
cites | cdi_FETCH-LOGICAL-c421t-a13cf6c3c07cf710ced558599dd8e44935c990a66d4ab89bf629231ab453e4b23 |
container_end_page | 599 |
container_issue | 2 |
container_start_page | 585 |
container_title | Journal of pest science |
container_volume | 91 |
creator | Poggi, Sylvain Le Cointe, Ronan Riou, Jean-Baptiste Larroudé, Philippe Thibord, Jean-Baptiste Plantegenest, Manuel |
description | A large-scale survey was carried out in 336 French fields to investigate the influence of soil characteristics, climate conditions, the presence of wireworms and the identity of predominant species, agricultural practices, field history and local landscape features on the damage caused by wireworms in maize. Boosted regression trees, a statistical model originating from the field of machine learning, were fitted to survey data and then used to hierarchize and weigh the relative influence of a large set of variables on the observed damage. Our study confirmed the relevance of an early assessment of wireworm populations to forecast crop damage. Results have shown that climatic factors were also major determinants of wireworm damage, especially the soil temperature around the sowing date, with a strong decrease in damage when it exceeds 12 °C. Soil characteristics were ranked third in importance with a primary influence of pH, but also of organic matter content, and to a lesser extent of soil texture. Field history ranked next; in particular, our findings confirmed that a long-lasting meadow appeared favourable to wireworm damage. Finally, agriculture practices and landscape context (especially the presence of a meadow in the field vicinity) were also shown to influence wireworm damage but more marginally. Overall, the predicted damage appeared highly correlated with the observed one allowing us to produce the framework of a decision support system to forecast wireworm risk in maize crop. |
doi_str_mv | 10.1007/s10340-018-0951-7 |
format | article |
fullrecord | <record><control><sourceid>proquest_hal_p</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_01840983v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2259205804</sourcerecordid><originalsourceid>FETCH-LOGICAL-c421t-a13cf6c3c07cf710ced558599dd8e44935c990a66d4ab89bf629231ab453e4b23</originalsourceid><addsrcrecordid>eNp9kU1LJDEQhhtxYdXdH7C3gCcPrZWv7uQo4hcMCLJ7Dul09dhjdzImPSPurzdDy3jSU4Xied9K1VsUfyicU4D6IlHgAkqgqgQtaVkfFEe0oqwUdVUd7t9S_SyOU1oBMA1cHRXNIw526rdIet8NG_QOSeiIG_rRTkisb4ldxoB-28fgR_STHUhn3RRiIsGT1z7ia4gjae1ol0hin56zFRlt_x-Ji2GdfhU_Ojsk_P1RT4p_N9d_r-7KxcPt_dXlonSC0am0lLuuctxB7bqagsNWSiW1bluFQmgundZgq6oVtlG66SqmGae2EZKjaBg_Kc5m3yc7mHXMC8Q3E2xv7i4XZtfLxxGgFd_SzJ7O7DqGlw2myazCJvr8PcOY1AykAvEtlY9eA68UZIrOVN42pYjdfjgFswvHzOHs5ptdOKbOGjZrUmb9EuOn89eidxyUkMc</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2259205804</pqid></control><display><type>article</type><title>Relative influence of climate and agroenvironmental factors on wireworm damage risk in maize crops</title><source>Springer Nature</source><creator>Poggi, Sylvain ; Le Cointe, Ronan ; Riou, Jean-Baptiste ; Larroudé, Philippe ; Thibord, Jean-Baptiste ; Plantegenest, Manuel</creator><creatorcontrib>Poggi, Sylvain ; Le Cointe, Ronan ; Riou, Jean-Baptiste ; Larroudé, Philippe ; Thibord, Jean-Baptiste ; Plantegenest, Manuel</creatorcontrib><description>A large-scale survey was carried out in 336 French fields to investigate the influence of soil characteristics, climate conditions, the presence of wireworms and the identity of predominant species, agricultural practices, field history and local landscape features on the damage caused by wireworms in maize. Boosted regression trees, a statistical model originating from the field of machine learning, were fitted to survey data and then used to hierarchize and weigh the relative influence of a large set of variables on the observed damage. Our study confirmed the relevance of an early assessment of wireworm populations to forecast crop damage. Results have shown that climatic factors were also major determinants of wireworm damage, especially the soil temperature around the sowing date, with a strong decrease in damage when it exceeds 12 °C. Soil characteristics were ranked third in importance with a primary influence of pH, but also of organic matter content, and to a lesser extent of soil texture. Field history ranked next; in particular, our findings confirmed that a long-lasting meadow appeared favourable to wireworm damage. Finally, agriculture practices and landscape context (especially the presence of a meadow in the field vicinity) were also shown to influence wireworm damage but more marginally. Overall, the predicted damage appeared highly correlated with the observed one allowing us to produce the framework of a decision support system to forecast wireworm risk in maize crop.</description><identifier>ISSN: 1612-4758</identifier><identifier>EISSN: 1612-4766</identifier><identifier>DOI: 10.1007/s10340-018-0951-7</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Agricultural land ; Agricultural practices ; Agriculture ; Biomedical and Life Sciences ; Cereal crops ; Climatic conditions ; Corn ; Crop damage ; Damage assessment ; Decision support systems ; Ecology ; Entomology ; Forestry ; Landscape ; Learning algorithms ; Life Sciences ; Machine learning ; Mathematical models ; Meadows ; Organic matter ; Original Paper ; Plant Pathology ; Plant Sciences ; Planting ; Polls & surveys ; Regression analysis ; Regression models ; Soil characteristics ; Soil conditions ; Soil investigations ; Soil properties ; Soil temperature ; Soil texture ; Soils ; Statistical analysis ; Statistical models ; Texture</subject><ispartof>Journal of pest science, 2018-03, Vol.91 (2), p.585-599</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2018</rights><rights>Copyright Springer Science & Business Media 2018</rights><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2018.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c421t-a13cf6c3c07cf710ced558599dd8e44935c990a66d4ab89bf629231ab453e4b23</citedby><cites>FETCH-LOGICAL-c421t-a13cf6c3c07cf710ced558599dd8e44935c990a66d4ab89bf629231ab453e4b23</cites><orcidid>0000-0003-3051-5091 ; 0000-0002-7964-1374</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://hal.science/hal-01840983$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Poggi, Sylvain</creatorcontrib><creatorcontrib>Le Cointe, Ronan</creatorcontrib><creatorcontrib>Riou, Jean-Baptiste</creatorcontrib><creatorcontrib>Larroudé, Philippe</creatorcontrib><creatorcontrib>Thibord, Jean-Baptiste</creatorcontrib><creatorcontrib>Plantegenest, Manuel</creatorcontrib><title>Relative influence of climate and agroenvironmental factors on wireworm damage risk in maize crops</title><title>Journal of pest science</title><addtitle>J Pest Sci</addtitle><description>A large-scale survey was carried out in 336 French fields to investigate the influence of soil characteristics, climate conditions, the presence of wireworms and the identity of predominant species, agricultural practices, field history and local landscape features on the damage caused by wireworms in maize. Boosted regression trees, a statistical model originating from the field of machine learning, were fitted to survey data and then used to hierarchize and weigh the relative influence of a large set of variables on the observed damage. Our study confirmed the relevance of an early assessment of wireworm populations to forecast crop damage. Results have shown that climatic factors were also major determinants of wireworm damage, especially the soil temperature around the sowing date, with a strong decrease in damage when it exceeds 12 °C. Soil characteristics were ranked third in importance with a primary influence of pH, but also of organic matter content, and to a lesser extent of soil texture. Field history ranked next; in particular, our findings confirmed that a long-lasting meadow appeared favourable to wireworm damage. Finally, agriculture practices and landscape context (especially the presence of a meadow in the field vicinity) were also shown to influence wireworm damage but more marginally. Overall, the predicted damage appeared highly correlated with the observed one allowing us to produce the framework of a decision support system to forecast wireworm risk in maize crop.</description><subject>Agricultural land</subject><subject>Agricultural practices</subject><subject>Agriculture</subject><subject>Biomedical and Life Sciences</subject><subject>Cereal crops</subject><subject>Climatic conditions</subject><subject>Corn</subject><subject>Crop damage</subject><subject>Damage assessment</subject><subject>Decision support systems</subject><subject>Ecology</subject><subject>Entomology</subject><subject>Forestry</subject><subject>Landscape</subject><subject>Learning algorithms</subject><subject>Life Sciences</subject><subject>Machine learning</subject><subject>Mathematical models</subject><subject>Meadows</subject><subject>Organic matter</subject><subject>Original Paper</subject><subject>Plant Pathology</subject><subject>Plant Sciences</subject><subject>Planting</subject><subject>Polls & surveys</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Soil characteristics</subject><subject>Soil conditions</subject><subject>Soil investigations</subject><subject>Soil properties</subject><subject>Soil temperature</subject><subject>Soil texture</subject><subject>Soils</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Texture</subject><issn>1612-4758</issn><issn>1612-4766</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kU1LJDEQhhtxYdXdH7C3gCcPrZWv7uQo4hcMCLJ7Dul09dhjdzImPSPurzdDy3jSU4Xied9K1VsUfyicU4D6IlHgAkqgqgQtaVkfFEe0oqwUdVUd7t9S_SyOU1oBMA1cHRXNIw526rdIet8NG_QOSeiIG_rRTkisb4ldxoB-28fgR_STHUhn3RRiIsGT1z7ia4gjae1ol0hin56zFRlt_x-Ji2GdfhU_Ojsk_P1RT4p_N9d_r-7KxcPt_dXlonSC0am0lLuuctxB7bqagsNWSiW1bluFQmgundZgq6oVtlG66SqmGae2EZKjaBg_Kc5m3yc7mHXMC8Q3E2xv7i4XZtfLxxGgFd_SzJ7O7DqGlw2myazCJvr8PcOY1AykAvEtlY9eA68UZIrOVN42pYjdfjgFswvHzOHs5ptdOKbOGjZrUmb9EuOn89eidxyUkMc</recordid><startdate>20180301</startdate><enddate>20180301</enddate><creator>Poggi, Sylvain</creator><creator>Le Cointe, Ronan</creator><creator>Riou, Jean-Baptiste</creator><creator>Larroudé, Philippe</creator><creator>Thibord, Jean-Baptiste</creator><creator>Plantegenest, Manuel</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><general>Springer Verlag</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SS</scope><scope>3V.</scope><scope>7X2</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>M0K</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0003-3051-5091</orcidid><orcidid>https://orcid.org/0000-0002-7964-1374</orcidid></search><sort><creationdate>20180301</creationdate><title>Relative influence of climate and agroenvironmental factors on wireworm damage risk in maize crops</title><author>Poggi, Sylvain ; Le Cointe, Ronan ; Riou, Jean-Baptiste ; Larroudé, Philippe ; Thibord, Jean-Baptiste ; Plantegenest, Manuel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c421t-a13cf6c3c07cf710ced558599dd8e44935c990a66d4ab89bf629231ab453e4b23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Agricultural land</topic><topic>Agricultural practices</topic><topic>Agriculture</topic><topic>Biomedical and Life Sciences</topic><topic>Cereal crops</topic><topic>Climatic conditions</topic><topic>Corn</topic><topic>Crop damage</topic><topic>Damage assessment</topic><topic>Decision support systems</topic><topic>Ecology</topic><topic>Entomology</topic><topic>Forestry</topic><topic>Landscape</topic><topic>Learning algorithms</topic><topic>Life Sciences</topic><topic>Machine learning</topic><topic>Mathematical models</topic><topic>Meadows</topic><topic>Organic matter</topic><topic>Original Paper</topic><topic>Plant Pathology</topic><topic>Plant Sciences</topic><topic>Planting</topic><topic>Polls & surveys</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Soil characteristics</topic><topic>Soil conditions</topic><topic>Soil investigations</topic><topic>Soil properties</topic><topic>Soil temperature</topic><topic>Soil texture</topic><topic>Soils</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Texture</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Poggi, Sylvain</creatorcontrib><creatorcontrib>Le Cointe, Ronan</creatorcontrib><creatorcontrib>Riou, Jean-Baptiste</creatorcontrib><creatorcontrib>Larroudé, Philippe</creatorcontrib><creatorcontrib>Thibord, Jean-Baptiste</creatorcontrib><creatorcontrib>Plantegenest, Manuel</creatorcontrib><collection>CrossRef</collection><collection>Entomology Abstracts (Full archive)</collection><collection>ProQuest Central (Corporate)</collection><collection>Agricultural Science Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>Agriculture Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Journal of pest science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Poggi, Sylvain</au><au>Le Cointe, Ronan</au><au>Riou, Jean-Baptiste</au><au>Larroudé, Philippe</au><au>Thibord, Jean-Baptiste</au><au>Plantegenest, Manuel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Relative influence of climate and agroenvironmental factors on wireworm damage risk in maize crops</atitle><jtitle>Journal of pest science</jtitle><stitle>J Pest Sci</stitle><date>2018-03-01</date><risdate>2018</risdate><volume>91</volume><issue>2</issue><spage>585</spage><epage>599</epage><pages>585-599</pages><issn>1612-4758</issn><eissn>1612-4766</eissn><abstract>A large-scale survey was carried out in 336 French fields to investigate the influence of soil characteristics, climate conditions, the presence of wireworms and the identity of predominant species, agricultural practices, field history and local landscape features on the damage caused by wireworms in maize. Boosted regression trees, a statistical model originating from the field of machine learning, were fitted to survey data and then used to hierarchize and weigh the relative influence of a large set of variables on the observed damage. Our study confirmed the relevance of an early assessment of wireworm populations to forecast crop damage. Results have shown that climatic factors were also major determinants of wireworm damage, especially the soil temperature around the sowing date, with a strong decrease in damage when it exceeds 12 °C. Soil characteristics were ranked third in importance with a primary influence of pH, but also of organic matter content, and to a lesser extent of soil texture. Field history ranked next; in particular, our findings confirmed that a long-lasting meadow appeared favourable to wireworm damage. Finally, agriculture practices and landscape context (especially the presence of a meadow in the field vicinity) were also shown to influence wireworm damage but more marginally. Overall, the predicted damage appeared highly correlated with the observed one allowing us to produce the framework of a decision support system to forecast wireworm risk in maize crop.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s10340-018-0951-7</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0003-3051-5091</orcidid><orcidid>https://orcid.org/0000-0002-7964-1374</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1612-4758 |
ispartof | Journal of pest science, 2018-03, Vol.91 (2), p.585-599 |
issn | 1612-4758 1612-4766 |
language | eng |
recordid | cdi_hal_primary_oai_HAL_hal_01840983v1 |
source | Springer Nature |
subjects | Agricultural land Agricultural practices Agriculture Biomedical and Life Sciences Cereal crops Climatic conditions Corn Crop damage Damage assessment Decision support systems Ecology Entomology Forestry Landscape Learning algorithms Life Sciences Machine learning Mathematical models Meadows Organic matter Original Paper Plant Pathology Plant Sciences Planting Polls & surveys Regression analysis Regression models Soil characteristics Soil conditions Soil investigations Soil properties Soil temperature Soil texture Soils Statistical analysis Statistical models Texture |
title | Relative influence of climate and agroenvironmental factors on wireworm damage risk in maize crops |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T14%3A57%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_hal_p&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Relative%20influence%20of%20climate%20and%20agroenvironmental%20factors%20on%20wireworm%20damage%20risk%20in%20maize%20crops&rft.jtitle=Journal%20of%20pest%20science&rft.au=Poggi,%20Sylvain&rft.date=2018-03-01&rft.volume=91&rft.issue=2&rft.spage=585&rft.epage=599&rft.pages=585-599&rft.issn=1612-4758&rft.eissn=1612-4766&rft_id=info:doi/10.1007/s10340-018-0951-7&rft_dat=%3Cproquest_hal_p%3E2259205804%3C/proquest_hal_p%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c421t-a13cf6c3c07cf710ced558599dd8e44935c990a66d4ab89bf629231ab453e4b23%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2259205804&rft_id=info:pmid/&rfr_iscdi=true |