Integrated Nutrient Management for Eggplant: Yield and Quality Models through Artificial Neural Network

Abstract

ABSTRACT Eggplant is a preferred vegetable for its nutritional quality and taste. India is the second largest eggplant producer in the world, but the productivity is far below the global average. The present experiment was conducted at the Bidhan Chandra Krishi Viswavidyalaya, West Bengal, India (23°N, 89°E, characteristically Aeric Haplaquept) for consecutive three years to assess the role of selected biofertilizer (Azospirillum, phosphate solubilizing bacteria or PSB) and micronutrient-fertilizer (iron sulfate, zinc sulfate and borax) application in augmenting yield and quality of eggplant (cv. F1-hybrid VNR-60). The experiment was laid out in a thrice replicated factorial randomized block design. Along with regular chemical fertilizers, application of bacterial fertilizers, alone as well as in combination with micronutrients, significantly improved the yield (13.10–15.35 t ha−1) over the control (9.83–12.39 t ha−1). Eggplant yield and quality models developed through artificial neutral network showed that fruit phosphorus (P) concentration was most important factor toward yield prediction followed by zinc (Zn) and soil available iron (Fe) concentration, respectively. Fruit nitrogen (N) concentration showed the highest sensitivity followed by soil available boron (B) and fruit potassium (K) concentration toward protein content. Application of B emerged to be most beneficial along with Azospirillum and PSB in attaining best yield, soil nutrient availabilities and plant nutritional recoveries.