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.

Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = ~0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = ~0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study 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 potential benefit of phenomic selection approaches in estimating breeding values before harvest.

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Plant height (PHT) in maize (Zea mays L.) has been scrutinized genetically and phenotypically due to relationship with other agronomically valuable traits (e.g. yield). Heritable variation of PHT is determined by many discovered quantitative trait loci (QTLs); however, phenotypic effects of such loci often lack validation across environments and genetic backgrounds, especially in the hybrid state grown by farmers rather than the inbred state more often used by geneticists. A previous genome wide association study using a topcrossed hybrid diversity panel identified two novel quantitative trait variants (QTVs) controlling both PHT and grain yield. Here, heterogeneous inbred families demonstrated that these two loci, characterized by two single nucleotide polymorphisms (SNPs), cause phenotypic variation in inbred lines, but that size of these effects were variable across four different genetic backgrounds, ranging from 1 to 10 cm. Weekly unoccupied aerial system flights demonstrated the two SNPs had larger effects, varying from 10 to 25 cm, in early growth while effects decreased towards the end of the season. These results show that allelic effect sizes of economically valuable loci are both dynamic in temporal growth and dynamic across genetic backgrounds, resulting in informative phenotypic variability overlooked following traditional phenotyping methods. Public genotyping data shows recent favorable allele selection in elite temperate germplasm with little change across tropical backgrounds. As these loci remain rarer in tropical germplasm, with effects most visible early in growth, they are useful for breeding and selection to expand the genetic basis of maize.

A major challenge of genetic improvement and selection is to accurately predict individuals with the highest fitness in a population without direct measurement. Over the last decade genomic predictions (GP) based on genome-wide markers have become reliable and routine. Now phenotyping technologies, including unoccupied aerial systems (UAS also known as drones), can characterize individuals with a data depth comparable to genomics when used throughout growth. This study, for the first time, demonstrated that the prediction power of temporal UAS phenomic data can achieve or exceed that of genomic data. UAS data containing red-green-blue (RGB) bands over fifteen growth time points and multispectral (RGB, red-edge and near infrared) bands over twelve time points were compared across 280 unique maize hybrids. Through cross validation of untested genotypes in tested environments (CV2), temporal phenomic prediction (TPP) outperformed GP (0.80 vs 0.71); TPP and GP performed similarly in three other cross validation scenarios. Genome wide association mapping using area under temporal curves of vegetation indices (VIs) revealed 24.5 % of a total of 241 discovered loci (59 loci) had associations with multiple VIs, explaining up to 51 % of grain yield variation, less than GP and TPP predicted. This suggests TPP, like GP, integrates small effect loci well improving plant fitness predictions. More importantly, temporal phenomic prediction appeared to work successfully on unrelated individuals unlike genomic prediction.