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Open access Jul 2026

Validating superior maize hybrids in all India coordinated trials using REMATTOOL-R: a decision support approach

Introduction The identification and advancement of superior maize hybrids under the All India Coordinated Research Project (AICRP) on Maize rely on multi-environment evaluation integrating grain yield, maturity, and agronomic performance. Interpretation of large multi-environment datasets is often complex, time-consuming, and susceptible to subjectivity, highlighting the need for objective and reproducible decision-support tools. This study evaluated the effectiveness of REMATTOOL-R (Relative Maturity Adjustment Tool in R) in validating the existing hybrid advancement framework adopted under the AICRP on Maize. Methods Multi-environment trial data from the National Initial Varietal Trial (NIVT)-Late conducted during Kharif 2020–21 across five locations representing the Central West Zone (CWZ) of India were analysed. The dataset comprised 45 entries, including 40 experimental hybrids, four commercial checks, and one filler entry. REMATTOOL-R integrated grain yield with days to 50% anthesis, grain moisture at harvest, and harvested plant stand to facilitate simultaneous evaluation of grain yield, maturity, and adaptation-related traits. Least-square means generated from mixed-model analysis were used to identify superior hybrids based on a predefined grain yield superiority threshold (≥5%) over the standard check while maintaining comparable maturity and agronomic performance. Results REMATTOOL-R enabled rapid visualization and integrated assessment of multiple agronomic traits, allowing objective identification of superior hybrids. Five experimental hybrids—PM 21109L (Entry 30), R8050 (Entry 35), PM 21111L (Entry 32), BIO 978 (Entry 4), and DKC 9226 (Entry 9)—recorded ≥5% higher grain yield than the standard check Bio 9682 while maintaining statistically comparable days to 50% anthesis, grain moisture at harvest, and harvested plant stand. All five hybrids identified by REMATTOOL-R corresponded with the official AICRP decisions for advancement from NIVT to Advanced Varietal Trial-I (AVT-I), while three hybrids (R8050, PM 21111L, and DKC 9226) progressed further to AVT-II during subsequent testing cycles, confirming the reliability of the analytical framework. Discussion The findings demonstrate that REMATTOOL-R provides an efficient, transparent, and reproducible framework for the simultaneous evaluation of grain yield, maturity, and adaptation-related traits in maize multi-environment trials. By complementing the existing AICRP hybrid evaluation procedure, the tool facilitates objective advancement decisions and reduces subjectivity associated with manual interpretation of complex datasets. REMATTOOL-R therefore represents a valuable decision-support approach for coordinated maize breeding programmes and has considerable potential for application in large-scale hybrid evaluation systems.

Sunil Neelam, Jyothi Bhoga, Jyostna Bellamkonda et al. · 0 citations
Jul 2026

Genomic dissection of stable meta-quantitative trait loci and candidate genes enabling durable disease and insect resistance in maize to safeguard food production.

BACKGROUND Maize is a globally important cereal crop that supports food and nutritional security and sustains livelihoods through its use as food, feed, and industrial raw material. However, maize productivity is severely constrained by destructive diseases and insect pests. Breeding for durable resistance is challenging due to the quantitative, polygenic, and environment-sensitive nature of these traits. To refine the genomic basis of resistance and identify robust breeding targets, a comprehensive meta-quantitative trait loci (M-QTL) analysis was conducted by integrating 528 quantitative trait loci (QTLs), comprising 368 disease-resistant and 160 insect-resistance QTLs. RESULTS The collected QTLs were consolidated into 74 stable M-QTLs, including 31 disease-specific (DI-MQTLs), 23 insect-specific (IN-MQTLs), and 20 co-localized M-QTLs (PL-MQTLs) conferring combined resistance to both stresses. Confidence intervals (CIs) were reduced by an average of 70.6% for disease-related and 51.2% for insect-related loci, with the identified M-QTLs showing a mean phenotypic variance explained (PVE) of 14.6%. Several PL-MQTLs, including PL-MQTL4.1 (CI = 2.91 cM; PVE = 23.0%) and PL-MQTL4.2 (CI = 0.84 cM; PVE = 23.1%), emerged as highly stable resistance hotspots. A total of 1884 candidate genes were identified, including those encoding NBS-LRR receptors, receptor-like kinases, transcription factors (WRKY, MYB, NAC, and AP2/ERF), peroxidases, cytochrome P450s, and benzoxazinoid-pathway genes. Key components of the salicylic acid (SA) and jasmonic acid (JA) signaling pathways co-localized within PL-MQTL regions, suggesting a mechanistic basis for broad-spectrum resistance. CONCLUSION The identified stable M-QTLs and prioritized candidate genes provide robust genomic resources for marker-assisted breeding, genomic prediction, and genome-editing approaches, thereby accelerating the development of durable, broad-spectrum disease- and insect-resistant maize cultivars. © 2026 Society of Chemical Industry.

Bhupender Kumar, Shrikant Yankanchi, Rakhi Singh et al. · 0 citations