Principle Component Analysis (PCA) and Character Interrelationship of Irrigated Blackgram [Vigna mungo (L.) Hepper] Influenced by Liquid Organic Biostimulants in Western Zone of Tamil Nadu

Abstract

Background: Blackgram [Vigna mungo (L.) Hepper] has significant agronomic and nutritional significance. Its productivity is insufficient to fulfil the expanding local demand in India. Increasing its productivity using appropriate agronomic practices is crucial. With this background, an experiment was conducted to study the effect of foliar application of liquid organic bio-stimulants on development, production and physiological characteristics of blackgram under irrigated conditions. Methods: Seven treatments comprising recommended dose of fertilizers (RDF) with foliar spray of dhasagavya, liquid rhizobium, fish amino acid, panchagavya, PPFM and seaweed extract at 1% and 3%, respectively were tested in randomised block design with three replications. The dimension of blackgram quantitative characters, viz., grain yield, plant height, number of branches per plant, dry matter production (DMP), leaf area index (LAI), number of pods per plant, number of seeds per pod, pod weight per plant, pod length, crop growth rate, total chlorophyll content, soluble protein content and nitrate reductase activity were reduced using principal component analysis (PCA). Result: The PCA was performed on all the attributes as correlation between the quantitative characters was found to be stronger among most of the biometric observations. It was noticed that almost 67% of the data’s total variability, as reflected by the first two principal components. It demonstrated that grain production, DMP, nitrate reductase activity, pods per plant and leaf area index were the primary contributors.

Forage Nutritive Quality, Yield and Quantitative Analysis of Leguminous Fodder Cowpea-Maize System as Influenced by Integrated Nutrient Management in Southern Zone of India

“…The variables under examination in this study include green forage yield (q/ha), plant height (cm), leaf stem ratio, c ru de fib er (%), eth er extract (%), ash (% ), carbohydrates (%), crude protein content (%), crude protein yield (q/ha) and dry matter yield (q/ha) (Ajaykumar et al, 2023). Correlation analysis was employed to unveil the associations among these variables, offering insights into their impact on green forage yield.…”

“…The goal of the multiple linear regressions was to quantitatively evaluate the relationships and clarify the extent of influence each specified parameter has on green forage yield (Ajaykumar et al, 2023). The resulting multiple linear regression equation is as follows:…”

Quantitative Variables Analysis, Growth and Yield of Groundnut (Arachis hypogaea L.) under Different Weed Management Practices

“…The study utilized correlation analysis to examine the relationships among several variables, including grain yield (kg/ha), plant height (cm), pods plant -1 (No. ), dry matter production (kg/ha), shelling (%), weed density (No./ m 2 ), weed biomass (Kg./m 2 ) and weed control efficiency (%) (Ajaykumar et al, 2023). It was computed using the equation, Where, r xy = Coefficient of the linear relationship between the variables x and y.…”

Productivity and Econometric Analysis of Leguminous Monkey-nut (Arachis hypogaea L.) as Influenced by Growth Retardant

“…This empirical evidence strongly supports the notion that an increase in these variables corresponds to an augmented groundnut yield. The objective of the multiple linear regressions was to quantitatively assess the relationships and elucidate the extent of influence each prescribed parameter exerts on pod yield (Ajaykumar et al, 2023). The multiple linear regression equation, consequently derived, is as follows: Productivity and Econometric Analysis of Leguminous Monkey-nut (Arachis hypogaea L.) as Influenced by Growth Retardant…”