International Journal of Plant Biology

Micronutrients, particularly boron (B), iron (Fe), manganese (Mn), and zinc (Zn), are pivotal for cotton (Gossypium spp.) growth, reproductive success, and fiber quality. However, their critical roles are often overlooked in fertility programs focused primarily on macronutrients. This review synthesizes recent advances in the physiological, molecular, and agronomic understanding of B, Fe, Mn, and Zn in cotton production. The overarching goal is to elucidate their impact on cotton nutrient use efficiency (NUE). Drawing from the peer-reviewed literature, we highlight how these micronutrients regulate essential processes, including photosynthesis, cell wall integrity, hormone signaling, and stress remediation. These processes directly influence root development, boll retention, and fiber quality. As a result, deficiencies in these micronutrients contribute to significant yield gaps even when macronutrients are sufficiently supplied. Key genes, including Boron Transporter 1 (BOR1), Iron-Regulated Transporter 1 (IRT1), Natural Resistance-Associated Macrophage Protein 1 (NRAMP1), Zinc-Regulated Transporter/Iron-Regulated Transporter-like Protein (ZIP), and Gossypium hirsutum Zinc/Iron-regulated transporter-like Protein 3 (GhZIP3), are crucial for mediating micronutrient uptake and homeostasis. These genes can be leveraged in breeding for high-yielding, nutrient-efficient cotton varieties. In addition to molecular hacks, advanced phenotyping technologies, such as unmanned aerial vehicles (UAVs) and single-cell RNA sequencing (scRNA-seq; a technology that measures gene expression at single-cell level, enabling the high-resolution analysis of cellular diversity and the identification of rare cell types), provide novel avenues for identifying nutrient-efficient genotypes and elucidating regulatory networks. Future research directions should include leveraging microRNAs, CRISPR-based gene editing, and precision nutrient management to enhance the use efficiency of B, Fe, Mn, and Zn. These approaches are essential for addressing environmental challenges and closing persistent yield gaps within sustainable cotton production systems.

Genome-wide association studies (GWAS) are essential for identifying genomic regions associated with agronomic traits, but Linear Mixed Model (LMM)-based GWAS face challenges in capturing complex gene interactions. This study explores the potential of machine learning (ML) methodologies to enhance marker identification and association modeling in plant breeding. Unlike LMM-based GWAS, ML approaches do not require prior assumptions about marker–phenotype relationships, enabling the detection of epistatic effects and non-linear interactions. The research sought to assess and contrast approaches utilizing ML (Decision Tree—DT; Bagging—BA; Random Forest—RF; Boosting—BO; and Multivariate Adaptive Regression Splines—MARS) and LMM-based GWAS. A simulated F₂ population comprising 1000 individuals was analyzed using 4010 SNP markers and ten traits modeled with epistatic interactions. The simulation included quantitative trait loci (QTL) counts varying between 8 and 240, with heritability levels set at 0.5 and 0.8. These characteristics simulate traits of candidate crops that represent a diverse range of agronomic species, including major cereal crops (e.g., maize and wheat) as well as leguminous crops (e.g., soybean), such as yield, with moderate heritability and a high number of QTLs, and plant height, with high heritability and an average number of QTLs, among others. To validate the simulation findings, the methodologies were further applied to a real Coffea arabica population (n = 195) to identify genomic regions associated with yield, a complex polygenic trait. Results demonstrated a fundamental trade-off between sensitivity and precision. Specifically, for the most complex trait evaluated (240 QTLs under epistatic control), Ensemble methods (Bagging and Random Forest) maintained a Detection Power (DP) exceeding 90%, significantly outperforming state-of-the-art GWAS methods (FarmCPU), which dropped to approximately 30%, and traditional Linear Mixed Models, which failed to detect signals (0%). However, this sensitivity resulted in lower precision for ensembles. In contrast, MARS (Degree 1) and BLINK achieved exceptional Specificity (>99%) and Precision (>90%), effectively minimizing false positives. The real data analysis corroborated these trends: while standard GWAS models failed to detect significant associations, the ML framework successfully prioritized consensus genomic regions harboring functional candidates, such as SWEET sugar transporters and NAC transcription factors. In conclusion, ML Ensembles are recommended for broad exploratory screening to recover missing heritability, while MARS and BLINK are the most effective methods for precise candidate gene validation.

Speed breeding technologies offer a promising avenue for accelerating crop improvement, yet their application to biennial crops like sugar beet remains constrained by extended generation cycles. This study examined the effects of supplemental phosphorus-potassium (PK) nutrition on the development of two hybrids under a speed-breeding protocol. Plants received one of four nutritional regimes: PK supplementation, potassium (K) supplementation, standard Knop’s solution (KS), or nutrient deficiency (D). Digital phenotyping confirmed that adequate nutrition maintained photosynthetic health, as deficiency significantly reduced NDVI and increased PSRI by 75 days. The most notable, genotype-specific effects were observed in reproductive architecture. PK nutrition significantly increased the median number of flower stalks by 17% in Smart Iberia KWS (21.0 vs. 18.0) and substantially in Dubravka KWS (33.0 vs. 1.0). PK also supported root development, increasing mini-steckling weight by 45–183% under white light. In the generative phase, plants under PK nutrition consistently showed the highest progression to flowering and capsule formation. A consistent increase in median 1000-seed weight of 24–36% was associated with PK treatment. In conclusion, supplementing standard nutrition with phosphorus and potassium enhances key yield-related architectural traits and supports reproductive development in sugar beet under speed-breeding conditions, with the magnitude of response depending on genotype. This provides a practical basis for optimizing mineral nutrition to improve the efficiency of accelerated breeding protocols. This provides a practical basis for optimizing mineral nutrition to improve the efficiency of speed breeding protocols.