Determining the Genetic Control of Common Bean Early-Growth Rate Using Unmanned Aerial Vehicles

Seed color patterns in domesticated common bean are regulated by MYB-bHLH-WD40 transcription factors and temperature

Seed colors and color patterns are critical for the survival of wild plants and the consumer appeal of crops. In common bean, a major global staple, these patterns are also essential in determining market classes, yet the genetic and environmental control of many pigmentation patterns remains unresolved. In this study, we genetically mapped variation for several important seed pattern loci, including T, Bip, phbw, and Z, which co-segregated with candidate genes PvTTG1, PvMYC1, PvTT8, and PvTT2, respectively. Proteins encoded by these genes are predicted to work together in MYB-bHLH-WD40 (MBW) complexes, propagating flavonoid biosynthesis across the seed coat as observed in Arabidopsis. Whole-genome sequencing of 37 accessions identified mutations, including seven unique parallel mutations in T (PvTTG1) and non-synonymous SNPs in highly conserved residues in bipana (PvMYC1) and z (PvTT2). A 612 bp intron deletion in phbw (PvTT8) eliminated motifs conserved since the Papilionoideae origin and corresponded to a 20-fold reduction in transcript abundance. In multi-location field trials of seven varieties with partial seed coat pigmentation patterning, the pigmented seed coat area correlated positively with ambient temperature, with up to 11-fold increases in the pigmented area from the coolest to the warmest environments. In controlled growth chamber conditions, an increase of 4°C was sufficient to cause pigmentation on an average additional 21% of the seed coat area. Our results shed light on key steps of flavonoid biosynthesis in common bean. They will inform breeding efforts for seed coat color/patterning to improve consumer appeal in this nutritious staple crop.

Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize

Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environments. This study utilized functional principal component analysis (FPCA) to summarize the variation of temporal VIs, uniquely allowing the integration of this data into phenomic prediction models tested across multiple years (2018-2021) and environments. The models, which included 1 genomic, 2 phenomic, 2 multikernel, and 1 multitrait type, were evaluated in 4 prediction scenarios (CV2, CV1, CV0, and CV00), relevant for plant breeding programs, assessing both tested and untested genotypes in observed and unobserved environments. Two hybrid populations (415 and 220 hybrids) demonstrated the visible atmospherically resistant index's strong temporal correlation with grain yield (up to 0.59) and plant height. The first 2 FPCAs explained 59.3 ± 13.9% and 74.2 ± 9.0% of the temporal variation of temporal data of VIs, respectively, facilitating predictions where flight times varied. Phenomic data, particularly when combined with genomic data, often were comparable to or numerically exceeded the base genomic model in prediction accuracy, particularly for grain yield in untested hybrids, although no significant differences in these models' performance were consistently observed. Overall, this approach underscores the effectiveness of FPCA and combined models in enhancing the prediction of grain yield and plant height across environments and diverse agricultural settings.

Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean

Proximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response. Bean plants, an important legume for human consumption, are often grown in regions with limited rainfall and irrigation and are therefore bred to further enhance drought tolerance. We assessed physiological (stomatal conductance and predawn and midday leaf water potential) and ground- and tower-based hyperspectral remote sensing (400 to 2,400 nm and 400 to 900 nm, respectively) measurements to evaluate drought response in 12 common bean and 4 tepary bean genotypes across 3 field campaigns (1 predrought and 2 post-drought). Hyperspectral data in partial least squares regression models predicted these physiological traits (R ² = 0.20 to 0.55; root mean square percent error 16% to 31%). Furthermore, ground-based partial least squares regression models successfully ranked genotypic drought responses similar to the physiologically based ranks. This study demonstrates applications of high-resolution hyperspectral remote sensing for predicting plant traits and phenotyping drought response across genotypes for vegetation monitoring and breeding population screening.