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Abstract Efficient partitioning of above and below-ground biomass in response to nitrogen (N) is critical to the productivity of plants under sub-optimal conditions. It is particularly essential in vegetable crops like spinach with shallow root systems, a short growth cycle, and poor nitrogen use efficiency. In this study, we conducted a genome-wide association study (GWAS) to explore N-induced changes using spinach accessions with diverse genetic backgrounds. We evaluated phenotypic variations as percent changes in the shoot and root biomass in response to N using 201 spinach accessions grown in randomized complete blocks design in a soilless media under a controlled environment. A GWAS was performed for the percent changes in the shoot and root biomass in response to N in the 201 spinach accessions using 60,940 whole-genome resequencing generated SNPs. Three SNP markers, chr4_28292655, chr6_1531056, and chr6_37966006 on chromosomes 4 and 6, were significantly associated with %change in root weight, and two SNP markers, chr2_18480277 and chr4_47598760 on chromosomes 2 and 4, were significantly associated with % change shoot weight. The outcome of this study established a foundation for genetic studies needed to improve the partitioning of total biomass and provided a resource to identify molecular markers to enhance N uptake via marker-assisted selection or genomic selection in spinach breeding programs.

Abstract The efficient acquisition and transport of nutrients by plants largely depend on the root architecture. Due to the absence of complex microbial network interactions and soil heterogeneity in a restricted soilless medium, the architecture of roots is a function of genetics defined by the soilless matrix and exogenously supplied nutrients such as nitrogen (N). The knowledge of root trait combinations that offer the optimal nitrogen use efficiency (NUE) is far from being conclusive. The objective of this study was to define the root trait(s) that best predicts and correlates with vegetative biomass under differed N treatments. We used eight image-derived root architectural traits of 202 diverse spinach lines grown in two N concentrations (high N, HN, and low N, LN) in randomized complete blocks design. Supervised random forest (RF) machine learning augmented by ranger hyperparameter grid search was used to predict the variable importance of the root traits. We also determined the broad-sense heritability (H) and genetic (r g ) and phenotypic (r p ) correlations between root traits and the vegetative biomass (shoot weight, SWt). Each root trait was assigned a predicted importance rank based on the trait’s contribution to the cumulative reduction in the mean square error (MSE) in the RF tree regression models for SWt. The root traits were further prioritized for potential selection based on the r g and SWt correlated response (CR). The predicted importance of the eight root traits showed that the number of root tips (Tips) and root length (RLength) under HN and crossings (Xsings) and root average diameter (RAvdiam) under LN were the most relevant. SWt had a highly antagonistic r g (− 0.83) to RAvdiam, but a high predicted indirect selection efficiency (− 112.8%) with RAvdiam under LN; RAvdiam showed no significant rg or rp to SWt under HN. In limited N availability, we suggest that selecting against larger RAvdiam as a secondary trait might improve biomass and, hence, NUE with no apparent yield penalty under HN.