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dc.contributor.advisorKhosla, Rajiv
dc.contributor.authorNaser, Mohammed Abdulridha
dc.contributor.committeememberHaley, Scott
dc.contributor.committeememberReich, Robin
dc.date.accessioned2007-01-03T08:34:16Z
dc.date.available2007-01-03T08:34:16Z
dc.date.issued2012
dc.description2012 Fall.
dc.descriptionIncludes bibliographical references.
dc.description.abstractPrecision agricultural practices have significantly contributed to the improvement of crop productivity and profitability. Remote sensing based indices, such as Normalized Difference Vegetative Index (NDVI) have been used to obtain crop information. It is used to monitor crop development and to provide rapid and nondestructive estimates of plant biomass, nitrogen (N) content and grain yield. Remote sensing tools are helping improve nitrogen use efficiency (NUE) through nitrogen management and could also be useful for high NUE genotype selection. The objectives of this study were: (i) to determine if active sensor based NDVI readings can differentiate wheat genotypes, (ii) to determine if NDVI readings can be used to classify wheat genotypes into grain yield productivity classes, (iii) to identify and quantify the main sources of variation in NUE across wheat genotypes, and (iv) to determine if normalized difference vegetation index (NDVI) could characterize variability in NUE across wheat genotypes. This study was conducted in north eastern Colorado for two years, 2010 and 2011. The NDVI readings were taken weekly during the winter wheat growing season from March to late June, in 2010 and 2011 and NUE were calculated as partial factor productivity and as partial nitrogen balance at the end of the season. For objectives i and ii, the correlation between NDVI and grain yield was determined using Pearson's product-moment correlation coefficient (r) and linear regression analysis was used to explain the relationship between NDVI and grain yield. The K-means clustering algorithm was used to classify mean NDVI and mean grain yield into three classes. For objectives iii and iv, the parameters related to NUE were also calculated to measure their relative importance in genotypic variation of NUE and power regression analysis between NDVI and NUE was used to characterize the relationship between NDVI and NUE. The results indicate more consistent association between grain yield and NDVI and between NDVI and NUE later in the season, after anthesis and during mid-grain filling stage under dryland and a poor association in wheat grown in irrigated conditions. The results suggest that below saturation of NDVI values (about 0.9), (i.e. prior to full canopy closure and after the beginning of senescence or most of the season under dryland conditions) NDVI could assess grain yield and NUE. The results also indicate that nitrogen uptake efficiency was the main source of variation of NUE among genotypes grown in site-years with lower yield. Overall, results from this study demonstrate that NDVI readings successfully classified wheat genotypes into grain yield classes across dryland and irrigated conditions and characterized variability in NUE across wheat genotypes.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierNaser_colostate_0053N_11491.pdf
dc.identifier.urihttp://hdl.handle.net/10217/74532
dc.languageEnglish
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019 - CSU Theses and Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectsoil science
dc.titleActive sensing: an innovative tool for evaluating grain yield and nitrogen use efficiency of multiple wheat genotypes
dc.typeText
dcterms.rights.dplaThe copyright and related rights status of this Item has not been evaluated (https://rightsstatements.org/vocab/CNE/1.0/). Please refer to the organization that has made the Item available for more information.
thesis.degree.disciplineSoil and Crop Sciences
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)


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