About

Professor Kelly Robbins is engaged in applying quantitative genetics, genomics, and computational science to improve the efficiency of crop breeding programs and to increase understanding of complex traits. Her research focuses on developing and optimizing breeding strategies for various crops, utilizing advanced statistical models, genomic prediction, and phenotypic data analysis. The Robbins Lab aims to accelerate genetic gains in crop improvement by integrating innovative genomic tools and data-driven approaches, contributing to sustainable agriculture and food security.

Research topics

• Computer Science
• Biotechnology
• Biology
• Genetics
• Agronomy
• Ecology
• Artificial Intelligence
• Computational biology
• Agroforestry

Selected publications

• Insights into biomass accumulation and challenges in grain yield prediction of elite breeding materials using UAV‐based vegetation indices in soft red winter wheat

  The Plant Phenome Journal · 2026-03-31

  articleOpen access

  Abstract High‐throughput phenotyping (HTP) techniques have brought new opportunities to understand and evaluate key traits in plant breeding programs. Combining multiple measures through time and random regression models permits a more comprehensive understanding of the genetic and environmental effects on trait expression over time. This study aims to understand the genetic basis of biomass accumulation in winter wheat and how this biomass is related to grain yield using unmanned aerial vehicle (UAV)‐based vegetation indices. A large panel of 596 soft red winter wheat genotypes was evaluated for agronomic performance in six environments to verify the ability of HTPs to predict grain yield using multivariate genomic prediction and random regression with Legendre polynomials to model growth through time. An additional set of 22 breeding lines was directly measured for above‐ground biomass, serving as a ground truth for the HTP‐derived biomass estimates. Cumulative vegetation indices were found to be a reliable method to infer biomass accumulation. Vegetation indices capture reliable phenotypes but exhibit low and inconsistent genetic correlation to grain yield, especially when incorporating residual covariance between traits. Predictive abilities of grain yield increased when using vegetation indices as a secondary trait in a multi‐trait genomic prediction model, but increases were highly variable across environments and growing stages, which may be confounded by micro‐environmental variation and lead to biased estimates of true genetic merit. Our results suggest that UAV‐based vegetation indices can be used to understand genetic parameters of biomass accumulation, but wheat breeders should use caution in their use as proxies for grain yield.

  PublisherDOI

• Inter-variety competition dynamics in US inbred and hybrid maize

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-02-28

  articleOpen accessSenior authorCorresponding

  ABSTRACT Variety mixtures provide a potential avenue in US cropping systems to improve yield stability and disease resistance. However, implementation of variety mixtures requires an understanding of the competitive dynamics of the crop. In this study, we examine the effects of plant competition both between and within plots through five unique experiments: 1) 5,000 diverse inbred lines in single-row plots, 2) hybrids in two-row plots developed from the above inbred lines, 3) over 4,000 hybrids measured in 141 locations in two-row plots as part of Genomes to Fields, 4) mixtures of two hybrids within a two-row plot planted across two years and five locations, and 5) mixtures of up to twenty hybrids in four-row plots in three locations. Across all experiments, we find that competitive interactions are extremely limited. Within inbred lines, height of the neighboring plot accounts for 1.2% of the variance in focal plot height. Similarly, neighbor height explains 1.7% of the variance in focal plot yield in hybrids developed from the inbred lines. The genetics of neighboring plots explains 1.55% of the variation in yield across 141 location-year environments, reinforcing the generally modest impacts of neighbor competition. In evaluating mixtures of hybrids in both two and four-row plots, we observe no yield penalty compared to conventional single hybrid plots, even with large height differentials of the hybrids included in the mixture or in mixtures of up to 20 hybrids within a plot. Finally, we observe that mixtures have more yield stability compared to conventional plots, highlighting a new avenue for increased stability in higher risk environments. The lack of yield penalty and stability benefits are promising for future investigations of mixtures that may complement each other in disease resistance or abiotic stress tolerance and increase overall yield stability in the field.

  PublisherOA PDFDOI

• BrAPI v2: real-world applications for data integration and collaboration in the breeding and genetics community

  JuSER Publikationsportal · 2025-01-01

  articleOpen access
  PublisherDOI

• Genomic prediction for general combining ability in hybrid canola ( <i>Brassica napa</i> L.)

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-04

  preprintSenior author

  Abstract Hybrid breeding is a method of selecting parental lines and determining crosses that are likely to yield the best hybrids. Genomic prediction (GP), a tool that uses genome wide markers, can be employed to predict the performance of untested hybrids based on general combining ability (GCA) of their parents. We investigated the potential of GP for GCA prediction in a commercial canola breeding program. We used female tester data, where many female lines are crossed with few male lines, to predict economically important traits in canola. Multi-year and location data for grain yield, oil, protein, days to flowering, days to maturity, total glucosinate and saturated fat were available for prediction. Three different cross-validation strategies were implemented to determine the predictive ability (PA) of each trait. In the first cross-validation scheme (CV1), a prediction model was validated using five-fold cross validation strategy. In the second cross-validation scheme (CV2), an unseen year was predicted using the previous years’ data as a training set. In the third cross-validation scheme (CV3), Inbred pe se performance was added to the training set to exploit the covariance between traits of inbred and hybrid trials. The highest PA was observed for CV1 while the lowest PA was seen for CV2. In CV1, PA ranged from 0.34 to 0.62. The highest PA was observed for protein (0.62) while the lowest PA was observed for days to maturity (0.34). For CV2, PA ranged from 0.16 to 0.46, while for CV3 PA ranged from 0.27 to 0.71. The highest PA for CV2 (0.46) and CV3 (0.71) was observed for total glucosinate while the lowest PA (0.16 CV2 and 0.27 -CV3) was for days to maturity. The current study demonstrates the potential of using marker information to select parents with the highest GCA to create the best hybrid combinations.

  PublisherDOI

• The effect of haplotype size on genomic selection accuracy and epistasis: An empirical study in rice

  The Plant Genome · 2025-11-27

  articleOpen access

  Genomic selection (GS) has revolutionized breeding practices by integrating genotype and phenotype data to predict genomic estimated breeding values, offering the potential to accelerate breeding cycles and intensify and enhance early-stage selections. This approach utilizes the concept of linkage disequilibrium (LD) between genetic markers and quantitative trait loci within populations. LD, the nonrandom association between alleles at different loci, provides valuable insights into historical recombination patterns, although it can change over time under strong selection or genetic drift. This study aimed to investigate the influence of recombination on haplotype sizes and LD, assess the impact of additive (A) versus additive + epistasis (A+I) genetic models on GS predictive ability (PA), and demonstrate how haplotype resolution in the training set (TS) impacts the PA of GS. For this, we used biparental (MP2) and multiparent (MP6-8) populations, where the main difference between them was the recombination rate. As expected, a strong correlation between LD decay and the number of recombination opportunities within populations was observed, with smaller haplotype blocks in populations experiencing more recombination. The use of A+I models increased heritability but did not improve PA. Finally, populations with smaller haplotype sizes in the TS exhibited enhanced PA. This study demonstrates the effect of haplotype size on GS accuracy, and its uniqueness lies in its focus on populations where the primary differentiating factor is haplotype size. It offers an important tool for breeders in designing GS strategies, providing valuable guidance for future breeding efforts.

  PublisherOA PDFDOI

• Reassessing data management in increasingly complex phenotypic datasets

  Trends in Plant Science · 2025-10-01 · 1 citations

  reviewOpen access

  Phenotypic datasets are increasingly rich and heterogeneous, with images, time courses, manual measurements, processed variables, and metadata. The management of such datasets navigates between partly incompatible objectives: (i) facilitate data analysis by extracting, organizing, and storing relevant variables; and (ii) allow reuse of raw, synthesized, and computed data (FAIR principles). For the first objective, 'dedicated datasets' can be extracted from raw information and tailored for the user's data analysis, but they result in a massive loss of information. We advocate that, for the second objective, 'sensu stricto phenomic datasets', upstream of dedicated datasets, should organize data without loss of information with data-science tools, in a 'theory-agnostic' way. They allow different users to build their own 'dedicated datasets' according to planned data analysis.

  PublisherDOI

• BrAPI v2: real-world applications for data integration and collaboration in the breeding and genetics community

  Database · 2025-01-01 · 4 citations

  articleOpen access

  Population growth and the impacts of climate change are placing increasing pressure on global agriculture and breeding programmes. Recent advancements in phenotyping techniques, genotyping technologies, and predictive modelling are accelerating genetic gains in breeding programmes, helping researchers and breeders develop improved crops more efficiently. However, these advancements have also led to an overwhelming torrent of fragmented data, creating significant challenges in data integration and management. To address this issue, the Breeding Application Programming Interface (BrAPI) project was established as a standardized data model for breeding data. BrAPI is an international, community-driven effort that facilitates interoperability among databases and tools, improving the sharing and interpretation of breeding-related data. This open-source standard is software-agnostic and can be used by anyone interested in breeding, phenotyping, germplasm, genotyping, and agronomy data management. This manuscript provides an overview of the BrAPI project, highlighting the significant progress made in the development of the data standard and the expansion of its community. It also presents a showcase of the wide variety of BrAPI-compatible tools that have been built to enhance breeding and research activities, demonstrating how the project is advancing agricultural innovation and data management practices.

  PublisherOA PDFDOI

• Development of an open-pollinated genetic mapping framework to facilitate the identification of QTL in apples

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-13

  preprintOpen accessSenior authorCorresponding

  Abstract Genetic mapping of traits in apples ( Malus domestica ), a temperate woody perennial, is challenging due to high heterozygosity, long juvenility, and extensive spatial maintenance requirements for the populations. These factors limit the effective use of traditional quantitative trait locus (QTL) mapping methods in identifying the genetic basis of traits critical for apple production, marketability and sustainability. We explored the use of open-pollinated (OP) genetic mapping as an alternative to conventional bi-parental QTL mapping and genome wide association studies (GWAS). A QTL was simulated and the performance of a mixed linear model (MLM) and a multi-locus mixed model (MLMM) in QTL mapping accuracy was compared using a genotyping-by-sequencing (GBS) dataset of seven interspecific Royal Gala × M. sieversii bi-parental F1 populations. The simulation results show that the MLMM outperformed the MLM by accurately identifying the simulated QTL. Analysis of power indicated that a population size of 137 individuals is required to reach an α = 0.8 for a simulated major effect QTL. Mapping resolution analysis showed that a population size of 470-600 individuals, depending on local recombination rates, is necessary to achieve high resolution within the OP population. Simulations demonstrate the potential of OP-based genetic mapping for identifying QTL in apples, reducing the logistical challenges associated with traditional QTL mapping methods. Our results show that OP-based genetic mapping could be used to speed up the identification of novel alleles directly from diverse germplasm collections in apples. Core Ideas Bi-parental QTL mapping in apples is constrained by limited diversity, high costs, and long-term orchard space. OP-based mapping avoids controlled pollination, clonal propagation, and large-scale F1 orchard requirements. OP-based mapping offers an alternative to bi-parental mapping by leveraging existing germplasm and natural crosses. OP-mapping enables QTL discovery absent full pedigree information to strategically capture broad genetic diversity. OP F1 populations support fast QTL detection, aiding rapid breeding against emerging pathogens.

  PublisherOA PDFDOI

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

  G3 Genes Genomes Genetics · 2024-08-21 · 3 citations

  articleOpen accessSenior authorCorresponding

  Multispectral imaging by unoccupied aerial vehicles provides a nondestructive, high-throughput approach to measure biomass accumulation over successive alfalfa (Medicago sativa L. subsp. sativa) harvests. Information from estimated growth curves can be used to infer harvest biomass and to gain insights into the relationship between growth dynamics and forage biomass stability across cuttings and years. In this study, multispectral imaging and several common vegetation indices were used to estimate genetic parameters and model growth of alfalfa cultivars to determine the longitudinal relationship between vegetation indices and forage biomass. Results showed moderate heritability for vegetation indices, with median plot level heritability ranging from 0.11 to 0.64, across multiple cuttings in three trials planted in Ithaca, NY, and Las Cruces, NM. Genetic correlations between the normalized difference vegetation index and forage biomass were moderate to high across trials, cuttings, and the timing of multispectral image capture. To evaluate the relationship between growth parameters and forage biomass stability across cuttings and environmental conditions, random regression modeling approaches were used to estimate the growth parameters of cultivars for each cutting and the variance in growth was compared to the variance in genetic estimates of forage biomass yield across cuttings. These analyses revealed high correspondence between stability in growth parameters and stability of forage yield. The results of this study indicate that vegetation indices are effective at modeling genetic components of biomass accumulation, presenting opportunities for more efficient screening of cultivars and new longitudinal modeling approaches that can provide insights into temporal factors influencing cultivar stability.

  PublisherDOI

• Muller’s Ratchet in Action: The Erosion of Sexual Reproduction Genes in Domesticated Cassava ( <i>Manihot esculenta</i> )

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-02-15 · 4 citations

  preprintOpen access

  Abstract Centuries of clonal propagation in cassava ( Manihot esculenta ) have engaged Muller’s Ratchet, leading to the accumulation of deleterious mutations due to the absence of sexual recombination. This has resulted in both inbreeding depression affecting yield and a significant decrease in reproductive performance, creating hurdles for contemporary breeding programs. Cassava is a member of the Euphorbiaceae family, including notable species such as rubber tree (Hevea brasiliensis) and poinsettia (Euphorbia pulcherrima). Expanding upon preliminary draft genomes, we annotated 7 long-read genome assemblies and aligned a total of 52 genomes, to analyze selection across the genome and the phylogeny. Through this comparative genomic approach, we identified 48 genes under relaxed selection in cassava. Notably, we discovered an overrepresentation of floral expressed genes, especially focused at six pollen-related genes. Our results indicate that domestication and a transition to clonal propagation has reduced selection pressures on sexually reproductive functions in cassava leading to an accumulation of mutations in pollen-related genes. This relaxed selection and the genome-wide deleterious mutations responsible for inbreeding depression are potential targets for improving cassava breeding, where the generation of new varieties relies on recombining favorable alleles through sexual reproduction.

  PublisherOA PDFDOI

Frequent coauthors

• Edward S. Buckler

  Cornell University

  29 shared

• Jean‐Luc Jannink

  Cornell University

  18 shared

• Nicolás Morales

  Cornell University

  17 shared

• Nicholas Santantonio

  Virginia Tech

  17 shared

• Michael A. Gore

  Cornell University

  16 shared

• M. Cinta Romay

  Cornell University

  14 shared

• Schery Umanzor

  University of Alaska Southeast

  13 shared

• Guilhem Sempéré

  Centre de Coopération Internationale en Recherche Agronomique pour le Développement

  13 shared

Labs

• The Robbins LabPI

  Applying quantitative genetics, genomics, and computational science to improve the efficiency of crop breeding programs and increase understanding of complex traits.