Growth, Yield and Profitability of Baby Corn (Zea mays L.) Hybrids as Influenced by Planting Density and Fertility Levels
V. Sujatha, I. Sudhir Kumar, M. Ramabhadra Raju
Journal of Experimental Agriculture International · pp. 760–770 · Published 11 Aug 2026
10.9734/jeai/2026/v48i84426Abstract
Background: Baby corn is a short-duration, high-value maize crop that provides nutritious cobs and valuable green fodder. Its productivity and profitability depend on appropriate planting density, nutrient management and hybrid selection under specific agro-climatic conditions. Aims: This study evaluated the effects of planting density, fertility level, and hybrid on the growth, phenology, yield, and profitability of baby corn under the coastal agro-climatic conditions of Andhra Pradesh. Study Design: A split-split plot design with three replications was used. Place and Duration of Study: The field experiment was conducted at the Agricultural Research Station, Peddapuram, Andhra Pradesh, India, during Kharif 2019. Methodology: Two planting densities (45 × 20 cm and 45 × 15 cm), two fertility levels [100% recommended dose of fertilisers (120:50:40 kg N:P₂O₅:K₂O ha⁻¹) and 150% recommended dose of fertilisers (180:75:60 kg N:P₂O₅:K₂O ha⁻¹)], and two hybrids (PAC 321 and VL Baby Corn 2) were evaluated. Growth, phenology, yield attributes, baby corn yield, green fodder yield, and economic returns were analysed using the least significant difference test at the 5% probability level. Results: Planting density and fertility level did not significantly affect most growth, phenological, or baby corn yield variables. The 45 × 15 cm spacing produced significantly greater green fodder yield than 45 × 20 cm. The 150% fertility level produced numerically higher baby corn yields than 100% RDF. PAC 321 recorded significantly greater cob size, baby corn yield with and without husk, net returns, and benefit–cost ratio, whereas VL Baby Corn 2 produced greater green fodder yield. Conclusion: PAC 321 showed the strongest yield and economic performance. The numerical advantages of closer spacing and 150% RDF require validation across seasons and locations before broader recommendation.
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