Unoccupied aerial systems temporal phenotyping and phenomic selection for maize breeding and genetics

Emerging tools in plant phenomics and high throughput field phenotyping are redefining possibilities for objective decision support in plant breeding and agronomy as well as discoveries in plant biology and the plant sciences. Unoccupied aerial systems (UAS, i.e. drones) have allowed inexpensive and rapid remote sensing for many genotypes throughout time in relevant field settings. UAS phenomics approaches have iterated rapidly, mimicking genomics progression over the last 30 years; the progression of UAS equipment parallels that of DNA-markers; while UAS analytics parallels progression from single marker linkage mapping to genomic selection. The TAMU maize breeding program first focused on using UAS to automate routine traits (plant height, plant population, etc.) comparing these to ground reference measurements. Finding success, we next focused on developing novel measurements impractical or impossible with manual collection such as plant growth and vegetation index curves. UAS plant growth curves measured in a genetic mapping populations has allowed discovery of temporal variation in quantitative trait loci (QTL). Now, phenomic selection approaches are being tested using temporal UAS, as first described using near infrared reflectance spectroscopy (NIRS) of grain. Phenomic selection is similar to genomic selection but uses a multitude of plant phenotypic measurements to identify relatedness and predict germplasm performance. Phenotypic measurements are thus treated as random markers with the underlying genetic or physiological cause remaining unknown. Using multiple extracted image features from multiple time points, genotype rankings have been successfully predicted for grain yield. Among the most exciting aspects have been identifying novel segregating physiological phenotypes important in prediction, which occur in growth stages earlier than previously evaluated. Similarly, UAS have allowed investigating plant responses to biotic and abiotic stress over time. UAS findings and approaches permit new fundamental plant biology and physiology research, which is catalyzing a new era in the plant sciences

Phenomic and Genomic Approaches to Understand Photoperiod Associated Flowering, Plant Height and Yield in Southern Maize (Zea Mays L.)

Tropical maize germplasm holds a wealth of diversity that could be used for crop improvement. Phenomic and genomic tools can help characterize phenotypes associated with both crop improvement as demonstrated here. Phenomics and genomics were used in this dissertation to characterize maize for crop improvement. Chapter I identified 7 loci, including three novel loci, that were linked to photoperiod-associated flowering in a novel recombinant inbred line (RIL) population derived from Tx773 and three temperate adapted lines (LH195, LH82 and PB80) grown in Texas, Wisconsin and Iowa in over three years. Chapter II showed that allelic effect sizes of economically valuable loci are both dynamic in temporal growth, resulting in characterizations of phenotypic variability overlooked traditional laborious phenotyping methods. Chapter III demonstrated how unoccupied aerial systems (UAS)-based phenotyping can reveal novel and dynamic relationships between time-specific associated loci with complex traits. These relationships were previously impractical to evaluate but doing so demonstrated many candidate genes putatively involve in the regulation of plant architecture even in early stages of maize growth and development. Chapter IV is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the benefit of phenomic selection approaches in estimating breeding values before harvest. Chapter V showed that (i) it is possible to predict complex traits using high throughput phenomic data between different managements and years, and that (ii) temporal phenotype data can reveal time-dependent association between RILs and abiotic stresses, to select resilient plants. Chapter VI showed that (i) complex traits can be predicted using the high throughput phenomic data between different managements and years, and (ii) temporal phenotype data can reveal time-dependent association between RILs and abiotic stress, which can help to select resilient plants. Chapter VII showed that when weather data was combined with temporal phenomic data, prediction abilities increased and were found to be more effective in yield prediction when tested and untested environments were less similar. Overall, temporal phenomic and weather data could moderately predict grain yield under the most challenging predictive breeding scenario of untested genotypes in untested environments

Pedigree‐management‐flight interaction for temporal phenotype analysis and temporal phenomic prediction

Abstract Unoccupied aerial systems (UAS, aka drones) provide high dimensional temporal phenotype data for predictive plant breeding and genetic dissection. Methods to assess temporal phenotype data are an emerging need to predict temporal breeding values of genotypes. Here a novel interaction design was developed and evaluated to include drone flight dates as a component into the mixed model; allowing the temporal changes of drone image derived traits of maize hybrids across different flight dates as well as different management conditions to be monitored. Across 2017 and 2019 respectively, 228 and 100 maize hybrids were grown under two types of management (optimal and late plantings). Seven drone surveys were conducted over each management in 2017 while five drone surveys were conducted over each management in 2019. Temporal plant height (canopy height measurements, CHM) and normalized green‐red difference index (NGRDI) were extracted from each drone survey and used as phenotype data to evaluate the interaction design. Day of flight effects explained the highest amount of total variation for grain yield in the interaction model, meaning the majority of phenotypic variation of CHM and NGRDI occurred across growth with a unique temporal trajectory in each management system. Temporal repeatability values remained higher than 0.5 for CHM and NGRDI in each year. Temporal CHM and NGRDI breeding values of maize hybrids were combined in ridge and lasso regression prediction models. Yield prediction ability of untested genotypes in untested environments were predicted higher by using pedigree × management × flight (PMF) and pedigree× management (PM) interaction results (∼0.34 and 0.52 in 2017 and 2019). Combining environment specific phenomic data (PMF plus PM) gave a larger improvement in yield prediction when the tested and untested environments were less similar. Overall, combined temporal phenomic data could moderately predict grain yield under the most challenging predictive breeding scenario, untested and unrelated genotypes in untested environments

The Nutritional Content of Common Bean (Phaseolus vulgaris L.) Landraces in Comparison to Modern Varieties

In terms of safe food and a healthy food supply, beans (Phaseolus spp.) are a significant source of protein, carbohydrates, vitamins and minerals especially for poor populations throughout the world. They are also rich in unsaturated fatty acids, such as linoleic and oleic acids. From the past to the present, a large number of breeding studies to increase bean yield, especially the common bean (P. vulgaris L.), have resulted in the registration of many modern varieties, although quality and flavor traits in the modern varieties have been mostly ignored. The aim of the present study, therefore, was to compare protein, fat, fatty acid, and some mineral content such as selenium (Se), zinc (Zn) and iron (Fe) of landraces to modern varieties. The landrace LR05 had higher mineral contents, particularly Se and Zn, and protein than the modern varieties. The landrace LR11 had the highest linoleic acid. The landraces are grown by farmers in small holdings for dual uses, such as both dry seed and snap bean production, and are commercialized with a higher cash price. The landraces of the common bean are, not only treasures that need to be guarded for the future, but also important genetic resources that can be used in bean breeding programs. The results of this study suggest that landraces are essential sources of important nutritional components for food security and a healthy food supply

Accuracies generated by different models with different regressions from training data optimization test.

The X-axes show “Percentage” of data used as training data and the Y-axes show the corresponding accuracies. For each model results from elastic net (EN), lasso (Lasso), ridge (Ridge), and random forest (RF) have been shown. The models tested were for–(a) time series CHM for vegetative growth stage, (b) time series CHM for reproductive stage, (c) ΣVI-SUMs for vegetative growth stage, (d) ΣVI-SUMs for reproductive stage, (e) ΣVI-AUCs for growth stage, and (f) ΣVI-AUCs for reproductive stage.</p

Accuracies generated by different models with different regressions from Group-III models, for vegetative reproductive stage.

(a) Model-III-1: ΣVI-SUMs and time series CHM for vegetative reproductive stage, (b) Model-III-2: ΣVI-AUCs and time series CHM for vegetative reproductive stage, (c) Model-III-3: ΣVI-SUMs and CHM of DAP 117th for vegetative reproductive stage, (d) Model-III-4: ΣVI-AUCs and CHM of DAP 117th for vegetative reproductive stage, (e) Model-III-5: ΣVI-SUMs for vegetative reproductive stage, (f) Model-III-6: ΣVI-AUCs for vegetative reproductive stage, and (g) Model-III-7: Time series of CHM for reproductive stage. Accuracies from elastic net (EN), lasso (Lasso), ridge (Ridge), and random forest (RF) models have been shown along the Y-axes.</p

Results of Group-I models.

The results include results from regressions for each model, and most important variables for each model for each regression type, and accuracies of each model for each regression type.</p