Cytogenetic and pathological investigations in maize × teosinte hybrids: Chromosome behaviour, spore identification, and inheritance of maydis leaf blight resistance

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Research Paper 12/08/2025
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Cytogenetic and pathological investigations in maize × teosinte hybrids: Chromosome behaviour, spore identification, and inheritance of maydis leaf blight resistance

Krishan Pal, Ravi Kishan Soni, Devraj, Rohit Kumar Tiwari, Ram Avtar
J. Biodiv. & Environ. Sci. 27(2), 70-76, August 2025.
Copyright Statement: Copyright 2025; The Author(s).
License: CC BY-NC 4.0

Abstract

Maize (Zea mays L.), a globally important cereal crop, is susceptible to numerous diseases, with Maydis Leaf Blight (MLB) caused by Bipolaris maydis posing a significant yield threat. This study investigated cytogenetic behavior, pathogen identification, and inheritance of MLB resistance in a maize × teosinte (Zea mays ssp. mexicana) crosses. The experiment was conducted in the subtropical Gangetic region under natural MLB incidence. F₂ populations (n=381) derived from an agronomically superior but susceptible maize parent and a resistant teosinte parent were evaluated. Cytological analysis of F₁ pollen at metaphase I revealed irregular pairing, with 9II+2I or 8II+4I configurations, indicating partial chromosomal homology between parents. Pathogen isolation from symptomatic F₂ leaves on potato dextrose agar confirmed B. maydis through colony morphology and characteristic olivaceous brown, spindle-shaped, multi-septate conidia. Disease scoring (0 to 5 scale) showed 58.53% resistant (score 1), 33.07% moderately resistant (score 2), and minimal susceptibility. Chi-square analysis of resistance vs. susceptibility (score ≥2) fit a 9:7 ratio (p<0.05), suggesting two-gene complementary epistasis controls MLB resistance. Descriptive statistics indicated low disease incidence (8.39%) and severity (9.41%) in the population. The results confirm teosinte as a valuable genetic resource for MLB resistance breeding in maize. Future studies under artificial epiphytotic conditions are recommended to validate findings and improve selection efficiency.

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