Investigations into using vegetative indices in soybean breeding - Clark, Randi R. (Kansas State University, 2016)
Yield in soybean (Glycine max (L.) Merr) needs to dramatically increase across the world to feed the growing population. Remote sensing and high-throughput phenotyping may provide a tool to better phenotype soybean genotypes. This research was conducted to: 1) examine the relationships between NDVI and CT with seed yield, maturity, lodging, and height, 2) determine if the time of day and growth stage have an effect on the spectral readings, 3) examine the relationships between spectral reflectance and traits associated with drought tolerance, and 4) evaluate how weather variables impact the ability of vegetative indices and canopy temperature to detect differences among genotypes. Ninety genotypes from the mapping population derived from the cross between KS4895 x Jackson were evaluated in Manhattan, KS, in 2013 and in McCune, Pittsburg, and Salina, KS in 2014. Genotypes were planted in a randomized complete bloc design in four-row, 3.4m long plots spaced 76 cm apart. Plant height, lodging, maturity and seed yield was collected on the center two rows of each plot. Spectral readings used to calculate a normalized differential vegetative index (NDVI) and canopy temperature (CT) were taken during reproductive growth. Nitrogen fixation trait and drought tolerance data was collected by the University of Arkansas. This population exhibited a substantial genetic variation for all traits evaluated. Correlations of NDVI and CT entry means with the agronomic traits were small and inconsistent. Time of day and growth stage were not important in differentiating genotypes. Differences in NDVI and CT did account for some genetic variation in drought tolerance traits, however, the strength of the associations were small. None of the weather variables were consistently associated with an increase or decrease in entry or error variance across the four environments. Stronger associations need to be established to use NDVI or CT to characterize differences in genotypes in a plant breeding program
Keywords: Soybean; NDVI; Weather; Remote Sensing