Location: Soil and Water Management ResearchTitle: Spectral reflectance models for characterizing winter wheat genotypes Author
|Ajayi, Sarah - Texas A&M University|
|Krishna Reddy, Srirama - Texas Agrilife Research|
|Xue, Qingwu - Texas Agrilife Research|
|Rudd, Jackie - Texas Agrilife Research|
|Pradhan, Gautham - North Dakota State University|
|Stewart, B.a. - West Texas A & M University|
|Liu, Shuyu - Texas Agrilife Research|
|Biradar, Chandra - International Center For Agricultural Research In The Dry Areas (ICARDA)|
|Jessup, Kirk - Texas Agrilife Research|
Submitted to: Journal of Crop Improvement
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/3/2016
Publication Date: 3/30/2016
Citation: Ajayi, S., Krishna Reddy, S., Gowda, P., Xue, Q., Rudd, J.C., Pradhan, G., Stewart, B., Liu, S.Y., Biradar, C., Jessup, K.E. 0206. Spectral reflectance models for characterizing winter wheat genotypes. Journal of Crop Improvement. 30(2):176-195.
Interpretive Summary: Genetic improvements in water yield by commonly grown crops on the Southern High Plains is one means of maintaining farm income as the availability of irrigation water from the Ogallala Aquifer decreases. In plant breeding programs, plant characteristics are measured at crucial stages and correlated to yield and crop water use for cultivar selection. It is an expensive and time consuming task to measure plant characteristics on hundreds of genotypes. Therefore, any simple, quick and automated method to measure plant characteristics is of high interest to plant breeders. High resolution remote sensing has the potential provide such tools. In this study, numerous remote sensing based statistical models were developed by scientists from ARS (Bushland, TX), Texas A&M AgriLife, West Texas A&M University and North Dakota State University. to measure plant characteristics such as leaf area index, biomass, and crop yield for cultivar selection purposes. These techniques will be of interest to plant breeders to screen large numbers of entries.
Technical Abstract: Optimum wheat yield can be achieved by developing and growing the best genotype in the most suited environment. However, exhaustive field measurements are required to characterize plants in breeder plots for screening genotypes with desirable traits. Remote sensing tools have been shown to provide relatively accurate and simultaneous measurements of plant characteristics without destructive sampling at low cost. The aim of this research was to develop and evaluate spectral reflectance based models for characterizing winter wheat genotypes in semiarid US Southern Great Plains (SGP). Field experiments were conducted at Bushland, TX during the 2011-2012 growing season. The spectral behavior of 20 wheat genotypes with wide genetic background was analyzed in relation to leaf area index (LAI) and yield under irrigated and dryland conditions. Reflectance based models were developed and evaluated using three approaches: the maximum correlations, the optimum multiple narrow band reflectance (OMNBR), and the vegetation indices (VIs). Results indicated that the combinations of two to four bands in OMNBR models explained most (65% to 89% and 51% to 95% for dryland and irrigated conditions, respectively) of the variability. Spectral regions in visible (VIS: 350-700 nm), near infrared (NIR: 700-1300 nm), and middle infrared (MIR: 1300-2500 nm) were sensitive to LAI and yield, most commonly the MIR. Models developed in this study are expected to assist in developing rapid and reliable methods for germplasm screening and selection of winter wheat genotypes in SGP. Further evaluation may be needed under different climatic conditions to refine the models and improve their accuracy.