Unoccupied aerial vehicle (UAV)‐based high‐throughput phenotyping provides scalable and cost‐effective access to phenotypic information for crop improvement, yet its application in minor crops such as lentil ( Lens culinaris Medik.) remains limited. This study applied UAV‐derived canopy traits and crop growth regression modeling to a nested association mapping population developed from CDC Redberry crossed with 32 diverse founder lines. UAV imagery collected across four site‐years was used to capture canopy height, crop area, and crop volume per plot basis at multiple time points. Crop growth regression models were fitted to derive crop growth parameters, maximum canopy size, growth rate, and cumulative growth anchored to phenological stages. These static and time‐series traits were evaluated for seed yield prediction using partial least squares regression with a 70:20:10 data split and 10‐fold cross‐validation. Static traits such as maximum crop volume and maximum crop area were consistently associated with yield. Dynamic trait‐based models improved prediction accuracy and identified the swollen pod stage (R5–R6) as the most informative forecasting window. External validation using an independent trial confirmed the generalizability of the approach. This study presents a UAV phenotyping framework that supports crop growth dissection and early yield prediction and downstream trait discovery in lentil.

Understanding the genetic basis of complex traits remains a key challenge in crop improvement. This study aimed to develop a structured, multi-parental mapping population to enhance the resolution of quantitative trait dissection, based on the hypothesis that a Nested Association Mapping (NAM) design would enable the detection of minor-effect loci often overlooked by traditional biparental or diversity panel-based approaches. A lentil ( Lens culinaris Medik.) NAM population was developed by crossing the Canadian cultivar CDC Redberry with 32 diverse genotypes sourced globally from three major lentil-growing macro-environments: Northern temperate, Mediterranean, and South Asia. The resulting recombinant inbred lines were phenotyped for key phenological traits, days to emergence (DTE), flowering (DTF), and maturity (DTM), under field conditions, and genotyped using exome capture sequencing. Genome-wide association studies for DTF identified 14 significant loci across six chromosomes, including the known FTb locus and novel associations near AP3a, HUB2a , and PIF6 genes. These results demonstrate the utility of the NAM design in detecting both major and minor-effect loci that underlie complex trait variation. To our knowledge, this is the first publicly available NAM population in lentil. It provides a high-resolution, globally representative platform for trait discovery, pre-breeding, and collaborative genetic improvement of this nutritionally and agronomically important legume. ### Competing Interest Statement The authors have declared no competing interest. Genome Canada, https://ror.org/029s29983 Genome Prairie, https://ror.org/013s3gf12 Western Grains Research Foundation, https://ror.org/00dn9nb13 Government of Saskatchewan University of Saskatchewan, https://ror.org/010x8gc63

The development of high-throughput phenotyping platforms to capture time-series data on large, diverse populations holds promise for crop researchers and breeders investigating growth-related traits. We used imagery from unoccupied aerial vehicles (UAVs) with red/green/blue (RGB) and multispectral cameras flown over multiple site-years in Saskatchewan, Canada, and Metaponto, Italy, to gather data for crop height, area, and volume in a lentil diversity panel (324 genotypes). The temporal nature of the UAV image-derived data enabled the modeling of growth curves for volume, height, and area, something that would be impractical under traditional phenotyping procedures in such a large population grown in multiple environments. A principal component analysis and hierarchical clustering revealed differential growth patterns across contrasting environments, with large variations in temperature and photoperiod, within our lentil diversity panel. Combining this analysis with genome-wide genotyping data, we identified markers, from an exome capture array (267,845 single nucleotide polymorphisms), associated with crop growth that could be used for marker-assisted selection. Our study demonstrates the potential for UAV-based imaging to obtain large-scale time-series data across multiple environments to model growth curves and investigate genotype-by-environment interactions. In addition, we can now use phenotypic traits that were once impractical to collect and derive novel phenotypes to improve our understanding of crop growth and the genetics underlying adaptation in lentil, approaches that will be useful for both researchers and breeders.

Adaptation constraints within crop species have resulted in limited genetic diversity in some breeding programs and/or areas where new crops have been introduced, e.g., lentil (Lens culinaris Medik.) in North America. An improved understanding of the underlying genetics involved in phenology-related traits is valuable knowledge to aid breeders in overcoming limitations associated with unadapted germplasm and expanding their genetic diversity by introducing new, exotic material. We used a large, 18 site-year, multi-environment dataset, phenotyped for phenology-related traits across nine locations and over three years, along with accompanying latent variable phenotypes derived from a photothermal model and principal component analysis (PCA) of days from sowing to flower (DTF) data for a lentil diversity panel (324 accessions) which has also been genotyped with an exome capture array. Genomewide association studies (GWAS) on DTF across multiple environments helped confirm associations with known flowering time genes and identify new quantitative trait loci (QTL), which may contain previously unknown flowering time genes. Additionally, the use of latent variable phenotypes, which can incorporate environmental data such as temperature and photoperiod as both GWAS traits and as covariates, strengthened associations, revealed additional hidden associations, and alluded to potential roles of the associated QTL. Our approach can be replicated with other crop species, and the results from our GWAS serve as a resource for further exploration into the complex nature of phenology-related traits across the major growing environments for cultivated lentil.

In recent decades, the field of phenomics has lagged behind the advances in genomics, which have become increasingly high-throughput and low-cost. In comparison, manually collected phenotypes are often time-consuming, labor intensive, and more costly to obtain. The development of high-throughput phenotyping platforms (HTPP) are bridging these gaps and enabling improved spatial and temporal resolution for researchers. We used imagery from unoccupied aerial vehicles (UAV) flown over multiple site years in Saskatchewan and Italy to gather data for crop height, area and volume in a lentil diversity panel. We found high correlations for our UAV-derived traits (height & volume) with our manually collected phenotypes (height & biomass). In addition, the high-throughput nature of the UAV allowed for the collection of time-series data which enabled the modelling of growth curves for volume, height and area, which would be impractical under traditional phenotyping procedures given the large population grown in multiple environments. Principal component analysis and hierarchical clustering revealed differential growth strategies amongst our diverse lentil population across contrasting environments. Our study demonstrates the potential for HTPP to obtain data that traditionally require destructive sampling, e.g., volume as a proxy for vegetative biomass, and improve the temporal quality of phenotype data enabling researchers to take their analysis beyond single time points, e.g., model growth curves. In addition, performing our analysis on data from contrasting environments, i.e., Saskatchewan and Italy, has helped elucidate optimal adaptation with regard to growth strategies in lentils.

Although lentil has a long history of cultivation, cultivars rely on a narrow genetic base, indicating room for broadening the diversity. Two field experiments were conducted at Bardiya, Nepal, during winter 2016 and 2017, with 324 diverse lentil genotypes obtained from genebanks and breeding programs around the world. Phenological traits related to adaptation, particularly days to flower, were assessed. A photothermal model was used to predict days to flower in new environments to identify genotypes that may be suitable for additional growing regions in Nepal, allowing for the expansion of the production area. Many putatively adapted genotypes were identified for terai, mid-hill, and high-hill growing regions. The list includes large-seeded or yellow cotyledon lines, representing new market classes of lentils for Nepal.

Societal Impact Statement Lentil is a staple in many diets around the world and growing in popularity as a quick‐cooking, nutritious, plant‐based source of protein in the human diet. Lentil varieties are usually grown close to where they were bred. Future climate change scenarios will result in increased temperatures and shifts in lentil crop production areas, necessitating expanded breeding efforts. We show how we can use a daylength and temperature model to identify varieties most likely to succeed in these new environments, expand genetic diversity, and give plant breeders additional knowledge and tools to help mitigate these changes for lentil producers. Summary Lentil (Lens culinaris Medik.) is cultivated under a wide range of environmental conditions, which has led to diverse phenological adaptations and resulted in a decrease in genetic variability within breeding programs due to reluctance in using genotypes from other environments. We phenotyped 324 genotypes across nine locations over three years to assess their phenological response to the environment of major lentil production regions and to predict days from sowing to flowering (DTF) using a photothermal model. DTF was highly influenced by the environment and is sufficient to explain adaptation. We were able to predict DTF reliably in most environments using a simple photothermal model, however, in certain site‐years, results suggest there may be additional environmental factors at play. Hierarchical clustering of principal components revealed the presence of eight groups based on the responses of DTF to contrasting environments. These groups are associated with the coefficients of the photothermal model and revealed differences in temperature and photoperiod sensitivity. Future climate change scenarios will result in increased temperature and/or shifts in production areas. The ability to use the photothermal model to identify genotypes most likely to succeed in these new environments has important social impacts with respect to traditional cropping systems.

Genomic selection (GS) is a type of marker-based selection which was initially suggested for livestock breeding and is being encouraged for crop breeding. Several statistical models and approaches have been developed to implement GS; however, none of these methods have been tested for use in lentil breeding. This study was conducted to evaluate different GS models and prediction scenarios based on empirical data and to make recommendations for designing genomic selection strategies for lentil breeding. We evaluated nine single-trait models, two multiple-trait models, and models that account for population structure and genotype-by-environment interaction (GEI) using a lentil diversity panel and two recombinant inbred lines (RIL) populations that were genotyped using a custom exome capture assay. Within-population, across-population and across-environment predictions were made for five phenology traits. Prediction accuracy varied among the evaluated models, populations, prediction scenarios, traits, and statistical models. Single-trait models showed similar accuracy for each trait in the absence of large effect QTL but BayesB outperformed all models when there were QTL with relatively large effects. Models that accounted for GEI and multiple-trait (MT) models increased prediction accuracy for a low heritability trait by up to 66% and 14% but accuracy did not improve for traits of high heritability. Moderate to high accuracies were obtained for within-population and across-environment predictions but across-population prediction accuracy was very low. This suggests that GS can be implemented in lentil to make predictions within populations and across environments, but across-population prediction should not be considered when the population size is small.

This article is written to disseminate descriptive results obtained from field experiments conducted at Bardiya, Nepal, during winter 2016 and 2017 on 324 diverse lentil ( L. culinaris Medik.) genotypes obtained from genebanks and breeding programs around the world. Lentil genotypes were examined for phenological traits related to adaptation, and recommendations for potential incorporation of new exotic genotypes into Nepalese breeding programs are presented. In addition, predictions of days to flowering based on temperature and photoperiod are used to identify genotypes that hold suitability for specific growing regions in Nepal, allowing for the potential expansion of growing regions. From this study we found many potentially adapted genotypes for terai, mid-hill and high hill growing regions, the list consists of a number of large-seeded lines with yellow cotyledons, an entirely new market class of lentils in Nepal. This paper primarily targets lentil breeders and agronomists; furthermore, it can be equally informative to extension workers involved in the pulse crop research and development in Nepal and other countries with similar climatic conditions.