In situ evaluation of stalk lodging resistance for different maize (

Cumulative lodging index Failure wind speed Lodging resistance Maize (Zea mays L.) Mechanical properties Wind machine

Journal

Plant methods
ISSN: 1746-4811
Titre abrégé: Plant Methods
Pays: England
ID NLM: 101245798

Informations de publication

Date de publication:
2019
Historique:
received: 17 04 2019
accepted: 09 08 2019
entrez: 28 8 2019
pubmed: 28 8 2019
medline: 28 8 2019
Statut: epublish

Résumé

Stalk lodging is an impediment to improving profitability and production efficiency in maize. Lodging resistance, a comprehensive indicator to appraise genotypes, requires both characterization of mechanical properties in laboratory and investigation of lodging percentage in field. However, in situ characterization of maize lodging resistance still remains poor. The aim of this study was to develop an indicator, named cumulative lodging index (CLI), based on lodging percentages at different wind speeds for evaluating lodging resistance for different maize cultivars, and to evaluate the accuracy and reliability of this method. Different cultivars showed different patterns of lodging percentage along with wind speeds. The failure wind speed (FWS) for maize ranged between 16 and 30 m s Our findings implied that mobile wind machine is powerful in reproducing wind disaster that induce crop lodging. The newly-built CLI was demonstrated to be a more robust indicator than mechanical properties, FWS, and RI when evaluating lodging resistance in terms of both reliability and resolution. This study offers a new perspective for evaluating in situ lodging resistance of crops, and provides technical support for accurate identification of lodging-resistant phenotypic traits.

Sections du résumé

BACKGROUND BACKGROUND
Stalk lodging is an impediment to improving profitability and production efficiency in maize. Lodging resistance, a comprehensive indicator to appraise genotypes, requires both characterization of mechanical properties in laboratory and investigation of lodging percentage in field. However, in situ characterization of maize lodging resistance still remains poor. The aim of this study was to develop an indicator, named cumulative lodging index (CLI), based on lodging percentages at different wind speeds for evaluating lodging resistance for different maize cultivars, and to evaluate the accuracy and reliability of this method.
RESULTS RESULTS
Different cultivars showed different patterns of lodging percentage along with wind speeds. The failure wind speed (FWS) for maize ranged between 16 and 30 m s
CONCLUSION CONCLUSIONS
Our findings implied that mobile wind machine is powerful in reproducing wind disaster that induce crop lodging. The newly-built CLI was demonstrated to be a more robust indicator than mechanical properties, FWS, and RI when evaluating lodging resistance in terms of both reliability and resolution. This study offers a new perspective for evaluating in situ lodging resistance of crops, and provides technical support for accurate identification of lodging-resistant phenotypic traits.

Identifiants

pubmed: 31452672
doi: 10.1186/s13007-019-0481-1
pii: 481
pmc: PMC6701094
doi:

Types de publication

Journal Article

Langues

eng

Pagination

96

Déclaration de conflit d'intérêts

Competing interestsThe authors declare that they have no competing interests.

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Auteurs

Weiliang Wen (W)

Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China.
2Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China.

Shenghao Gu (S)

Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China.
2Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China.

Boxiang Xiao (B)

Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China.
2Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China.

Chuanyu Wang (C)

Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China.
2Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China.

Jinglu Wang (J)

Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China.
2Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China.

Liming Ma (L)

Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China.
2Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China.

Yongjian Wang (Y)

Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China.
2Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China.

Xianju Lu (X)

Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China.
2Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China.

Zetao Yu (Z)

Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China.
2Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China.

Ying Zhang (Y)

Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China.
2Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China.

Jianjun Du (J)

Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China.
2Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China.

Xinyu Guo (X)

Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China.
2Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China.

Classifications MeSH