Ace Pugh, Ph.D. is a postdoctoral research associate in Dr. Duke Pauli’s laboratory in the School of Plant Sciences at the University of Arizona. Ace’s primary research interest is high-throughput phenotyping of economically important crop species, particularly through the use of unoccupied aerial systems (UAS), or drones.
Ace earned his Ph.D. in the laboratory of Dr. William L. Rooney in the Sorghum Breeding Program at Texas A&M University. His work involved the use of UAS to phenotype several important characteristics of sorghum including plant height, anthracnose disease presence and severity, and biomass yield. In addition, he worked on a project that used proximal sensing of the soil to investigate spatial variation in sorghum trials.
As a postdoctoral associate, Ace is researching the optimization of georeferencing methods for UAS studies, phenotyping methodologies for drought stress in lettuce, efficient methods of phenotyping important traits in cotton, and more.
Ph.D. Plant Breeding, 2018
Texas A&M University
M.S. Plant Breeding, 2015
Texas A&M University
B.S. Biology, 2012
University of Central Oklahoma
Success of hybrid performance trials has always been susceptible to the efficiency at which the experimental design can remove any effect of spatial autocorrelation associated with environmental factors. Blocking in a randomized design is one way of accounting for this. Another method is to have a model of the environmental variability. Measures of soil variability could be useful to represent spatial structure in a trial. Soil apparent electrical conductivity (ECa) measurements can be collected rapidly and non-invasively and have been well documented to be able to map soil variability at the meter scale. We present a statistical evaluation that compares the effectiveness of the traditional replicated block designs with spatially explicit soil ECa measurements. Soil ECa, sorghum (Sorghum bicolor (L.) Moench) grain yield, and plant height were measured from six sorghum hybrid evaluation trials across Texas in 2017. Three linear models were tested to determine the presence or absence of spatial autocorrelation of model residuals within each performance trial. Moran’s I tests on model residuals showed that neither method was consistently effective in accounting for spatial variability. Blocking was more effective at one site for both plant height and grain yield, whereas ECa data were more effective at two sites for grain yield only. Based on these results, and the relatively low cost of using both methods together, we propose that plant breeders interested in addressing spatial autocorrelation in models from trial results may consider using both methods and select the best model, post hoc.
To meet future world food and fiber demands, plant breeders must increase the rate of genetic improvement of important agricultural crops. One of the biggest obstacles now facing crop scientists is a phenotyping bottleneck. To ease this burden, the emerging technology of unmanned aerial systems (UAS) presents an exciting opportunity. To assess the utility of UAS, it is important to investigate their application across multiple crop species. Terminal plant height is of great importance to maize (Zea mays L.) and sorghum [Sorghum bicolor (L.) Moench] breeders and has been hypothesized to be useful but has been logistically impractical to measure in the field. In this study, we statistically analyzed in depth the ability of UAS to estimate height in sorghum (advanced and early generation material) and maize (optimal and late material) and the application of these estimates in breeding programs. We found that UAS explain genotypic variation similarly to ground-truth methods and that the repeatability of the methodology is high (R = 0.61–0.99), indicating effective differentiation of genotypes. Additionally, correlations between ground truth and UAS measurements were moderate to high for all materials (r = 0.4–0.9). Finally, we present a novel application for the technology in the form of high-resolution temporal growth curves. Using these UAS-generated growth curves, new physiological insights can be obtained and new avenues of scientific investigation are possible.