Biology subjects

Wickes-Do, L.

Publications and source records attributed to Wickes-Do, L..

3 recordsLinked to original sources

bioRxiv2026

Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials

Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.

genetics↗

bioRxiv2025

Investigating the role of stomatal dynamics on agronomic traits using a slac1-2 Zea mays mutant

The production of staple food crops is becoming increasingly difficult due to the amount of freshwater needed to realize food security for a growing global population. Climate projections show hotter and drier growing seasons in traditionally productive agricultural regions, which will increase the demand for limited water resources. Thus, strategies to improve water use efficiency will only become more important for sustainable agriculture. At the leaf level, stomatal conductance has a large influence on transpirational water loss and therefore water use efficiency. The anion channel SLAC1 has been shown in several plant species to function as the primary mechanism for stomatal closure, which allows stomatal aperture to change dynamically in response to environmental stimuli. Given that slac1 is a single gene that can significantly alter stomatal conductance, it has the potential to serve as a control point for improving water use efficiency. Here we fully characterize a Zea mays slac1-2 mutant in multiple field environments. Interestingly, homozygous slac1-2 hybrids did not show improved net CO2 assimilation or increased grain yield despite having greater stomatal conductance compared to a wild-type hybrid. Net CO2 assimilation and grain yield were either lower or similar in slac1-2 compared to wild-type across environments. Furthermore, the slac1-2 hybrid did not have increased nitrogen uptake. These results suggest that the C4 carbon concentrating mechanism removes any stomatal conductance limitations to CO2 assimilation, even in highly productive wild-type Z. mays hybrids. Because the slac1-2 hybrids eliminate stomatal conductance as a major variable for modulating water loss, future studies will be able to investigate alternate regulators of plant water potential to identify novel mechanisms for increasing water use efficiency.

physiology↗

bioRxiv2024

Remote sensing for estimating genetic parameters of biomass accumulation and modeling stability of growth curves in alfalfa

Multi-spectral imaging (MSI) collection by unoccupied aerial vehicles (UAV) is an important tool to measure growth of forage crops. Information from estimated growth curves can be used to infer harvest biomass and to gain insights in the relationship of growth dynamics and harvest biomass stability across cuttings and years. In this study, we used MSI to evaluate Alfalfa (Medicago sativa L. subsp. sativa) to understand the longitudinal relationship between vegetative indices (VIs) and forage/biomass, as well as evaluation of irrigation treatments and genotype by environment interactions (GEI) of different alfalfa cultivars. Alfalfa is a widely cultivated perennial forage crop grown for high yield, nutritious forage quality for feed rations, tolerance to abiotic stress, and nitrogen fixation properties in crop rotations. The direct relationship between biomass and VIs such as Normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red edge normalized difference vegetation index (NDRE), and Near infrared (NIR) provide a non-destructive and high throughput approach to measure biomass accumulation over subsequent alfalfa harvests. In this study, we aimed to estimate the genetic parameters of alfalfa VIs and utilize longitudinal modeling of VIs over growing seasons to identify potential relationships between stability in growth parameters and cultivar stability for alfalfa biomass yield across cuttings and years. We found VIs of GNDVI, NDRE, NDVI, NIR and simple ratios to be moderately heritable with median values for the field trial in Ithaca, NY to be 0.64, 0.56, 0.45, 0.45 and 0.40 respectively, Normal Irrigation (NI) trial in Leyendecker, NM to be 0.3967, 0.3813, 0.3751, 0.3239 and 0.3019 respectively, and Summer Irrigation Termination (SIT) trial in Leyendecker, NM to be of 0.11225, 0.1389, 0.1375, 0.2539 and 0.1343, respectively. Genetic correlations between NDVI and harvest biomass ranged from 0.52 - .99 in 2020 and 0.08 - .99 in 2021 in the NY trial. Genetic correlations for NI trial in NM for NDVI ranged from 0.72 - .98 in 2021 and SIT ranged from 0.34-1.0 in 2021. Genotype by genotype by interaction (GGE) biplots were used to differentiate between stable and unstable cultivars for locations NY and NM, and Random regression modeling approaches were used to estimate growth parameters for each cutting. Results showed high correspondence between stability in growth parameters and stability, or persistency, in harvest biomass across cuttings and years. In NM, the SIT trial showed more variation in growth curves due to stress conditions. The temporal growth curves derived from NDVI, NIR and Simple ratio were found to be the best phenotypic indices on studying the stability of growth parameters across different harvests. The strong correlation between VIs and biomass present opportunities for more efficient screening of cultivars, and the correlation between estimated growth parameters and harvest biomass suggest longitudinal modeling of VIs can provide insights into temporal factors influencing cultivar stability.

genetics↗