Please use this identifier to cite or link to this item: http://repository.afs.edu.gr/handle/6000/254
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dc.contributor.advisorGertsis, Athanasiosen_US
dc.contributor.authorOroilidis, Konstantinosen_US
dc.date.accessioned2019-09-15T16:59:25Z-
dc.date.available2019-09-15T16:59:25Z-
dc.date.issued2017-06-
dc.identifier.urihttp://repository.afs.edu.gr/handle/6000/254-
dc.identifier.urihttps://librarycatalog.afs.edu.gr/cgi-bin/koha/opac-detail.pl?biblionumber=22259en_US
dc.descriptionIncludes bibliographical references, charts and illustrationsen_US
dc.description.abstractCotton can be named as one of the most significant crops all over the United States. It is cultivated in 17 different US states with a planting area of 8.5 million acres (3.440.000 hectares). It can be generally mentioned as a demanding crop in term of inputs, with high requirements on plant growth regulators (PGRs) so reproductive and vegetative growth can be balanced, respectively. In addition it has needs concerning sufficient amounts of nitrogen so rank growth can be obstructed. Finally, in order mechanical harvesting to be achieved at the end of the season, cotton is in need of defoliant application. Therefore, the 40% of the produced cotton in United States is irrigated, while in Georgia State, approximately the 50% of the produced cotton is grown under irrigated conditions. Nowadays, due to the fact that irrigation water has become limited in pretty notable cotton growing states in combination with the rapidly raised water competition in areas of abundant water resources, cotton producers are seeking methods which will increase water use efficiency. However, only the 1/5 of producers is applying science-based irrigation scheduling within their cotton fields. On the other hand, a huge amount of them is still taught to depend on visual cues, plant stress (wilting) and fixed irrigation scheduling methods For all of the above, the main goal of this BSc thesis is to compare and evaluate three different irrigation scheduling methods or else strategies within conventional and conservation tillage systems. The previously mentioned irrigation scheduling strategies are "SmartIrrigation Cotton App", "University of Georgia Checkbook Method for Cotton" and finally the "University of Georgia Smart Sensor Array". The experimental study was conducted during the growing season of 2016 at the University of Georgia's Stripling Irrigation Research Park in Camilla, Georgia. USA. Surprisingly the growing season was mentioned as wet since 25.55 inches (649 mm) of rain were recorded. Finally, the highest yield was obtained from SSA Constant kPa Conventional and Rainfed Conservation treatments as well. The main conclusion of the study is that there are still many things to be learned from experiments under such weather conditions.en_US
dc.formatSpiral bindingen_US
dc.format.extent89 pagesen_US
dc.language.isoenen_US
dc.publisherPerrotis Collegeen_US
dc.publisherCardiff Metropolitan Universityen_US
dc.rightsAll rights reserveden_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectCotton growingen_US
dc.subjectNormalized difference vegetation index (NDVI)en_US
dc.subject.lcshDissertations, Academicen_US
dc.subject.lcshCotton growingen_US
dc.subject.lcshAgriculture - Remote sensingen_US
dc.subject.otherPerrotis College - Dissertations - 2017en_US
dc.subject.otherPrecision agriculture. Perrotis Collegeen_US
dc.titlePrecision irrigation applications for cotton (Gossypium hirsutum L.), at university of Georgia, Tifton campus, Tifton Georgia, USAen_US
dc.typeDissertationen_US
local.description.statusnot publisheden_US
local.repositoryDAPLen_US
Appears in Collections:Dissertations

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