Thermal imaging for assessment of maize water stress and yield prediction under drought conditions

dc.contributor.authorPradawet, Chukiat
dc.contributor.authorKhongdee, Nuttapon
dc.contributor.authorPansak, Wanwisa
dc.contributor.authorSpreer, Wolfram
dc.contributor.authorHilger, Thomas
dc.contributor.authorCadisch, Georg
dc.date.accessioned2024-09-03T08:32:05Z
dc.date.available2024-09-03T08:32:05Z
dc.date.issued2022de
dc.description.abstractMaize production in Thailand is increasingly suffering from drought periods along the cropping season. This creates the need for rapid and accurate methods to detect crop water stress to prevent yield loss. The study was, therefore, conducted to improve the efficacy of thermal imaging for assessing maize water stress and yield prediction. The experiment was carried out under controlled and field conditions in Phitsanulok, Thailand. Five treatments were applied, including (T1) fully irrigated treatment with 100% of crop water requirement (CWR) as control; (T2) early stress with 50% of CWR from 20 days after sowing (DAS) until anthesis and subsequent rewatering; (T3) sustained deficit at 50% of CWR from 20 DAS until harvest; (T4) late stress with 100% of CWR until anthesis and 50% of CWR after anthesis until harvest; (T5) late stress with 100% of CWR until anthesis and no irrigation after anthesis. Canopy temperature (FLIR), crop growth and soil moisture were measured at 5‐day‐intervals. Under controlled conditions, early water stress significantly reduced maize growth and yield. Water deficit after anthesis had no significant effect. A new combination of wet/dry sponge type reference surfaces was used for the determination of the Crop Water Stress Index (CWSI). There was a strong relationship between CWSI and stomatal conductance (R² = 0.90), with a CWSI of 0.35 being correlated to a 64%‐yield loss. Assessing CWSI at 55 DAS, that is, at tasseling, under greenhouse conditions corresponded best to the final maize yield. This linear regression model validated well in both maize lowland (R² = 0.94) and maize upland fields (R² = 0.97) under the prevailing variety, soil and climate conditions. The results demonstrate that, using improved standardized references and data acquisition protocols, thermal imaging CWSI monitoring according to critical phenological stages enables yield prediction under drought stress.en
dc.identifier.swb1789506972
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/16343
dc.identifier.urihttps://doi.org/10.1111/jac.12582
dc.language.isoengde
dc.rights.licensecc_by-ncde
dc.source1439-037Xde
dc.sourceJournal of agronomy and crop science; Vol. 209, No. 1 (2022), 56-70de
dc.subjectCWSI
dc.subjectStomatal conductance
dc.subjectThailand
dc.subjectWater deficit irrigation
dc.subjectZea mays
dc.subject.ddc630
dc.titleThermal imaging for assessment of maize water stress and yield prediction under drought conditionsen
dc.type.diniArticle
dcterms.bibliographicCitationJournal of agronomy and crop science, 209 (2022), 1, 56-70. https://doi.org/10.1111/jac.12582. ISSN: 1439-037X
dcterms.bibliographicCitation.issue1
dcterms.bibliographicCitation.journaltitleJournal of agronomy and crop science
dcterms.bibliographicCitation.volume209
local.export.bibtex@article{Pradawet2022, doi = {10.1111/jac.12582}, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16343}, author = {Pradawet, Chukiat and Khongdee, Nuttapon and Pansak, Wanwisa et al.}, title = {Thermal imaging for assessment of maize water stress and yield prediction under drought conditions}, journal = {Journal of agronomy and crop science}, year = {2022}, volume = {209}, number = {1}, }
local.export.bibtexAuthorPradawet, Chukiat and Khongdee, Nuttapon and Pansak, Wanwisa et al.
local.export.bibtexKeyPradawet2022
local.export.bibtexType@article

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