Open Access Open Access  Restricted Access Subscription Access

Soil enzymatic activity, nutrient dynamics and biplot analysis under varied plant population and nutrient management in machine-planted chickpea


Affiliations
1 Department of Agronomy, College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad 500 030, India
2 AICRP on STCR Scheme, Professor Jayashankar Telangana State Agricultural University, Hyderabad 500 030, India
3 Department of Bio-Energy and Microbiology, Professor Jayashankar Telangana State Agricultural University, Hyderabad 500 030, India
4 AICRP-FIM Scheme, Professor Jayashankar Telangana State Agricultural University, Hyderabad 500 030, India

Soil dehydrogenase (14.2, 11.3 mg TPF g–1 day–1), alkaline phosphatase (109.5, 86.7 mg PNP g–1 soil h–1), acid phosphatase (69.7, 51.6 mg PNP g–1 soil h–1) and urease activity (60.4, 39.9 mg NH4 g–1 2 h–1) in chickpea at flowering and harvest with seed rate @ 105 kg ha–1. Among, nutrient management the corresponding activity (14.9, 11.7 mg TPF g–1 day–1), (120.7, 96.7 mg g PNP g–1 soil h–1), (70.5, 52.7 mg g PNP g–1 soil h–1) was higher with 75% RDF + microbial consortia (MC). Contrarily, urease activity (62.3, 38.7 mg g NH4 g–1 2 h–1), soil available soil nitrogen (181.3, 179.0 kg ha–1) and phosphorus (78.3, 76.5 kg ha–1) were higher under 125% RDF + MC. PCA indicated that among nutrient management, first principal component explained 71.37% variability to urease activity, available soil potassium and dehydrogenase activity and second component (22.34%) to available soil phosphorus and nitrogen. Among planting density, first component explained variability (66.87%) to acid phosphatase and second component (32.11%) to available phosphorus

Keywords

Enzyme activity, nutrient management, PCAbiplot, planting density, soil nutrient dynamics.
User
Notifications
Font Size

Abstract Views: 190




  • Soil enzymatic activity, nutrient dynamics and biplot analysis under varied plant population and nutrient management in machine-planted chickpea

Abstract Views: 190  | 

Authors

M. Karthika
Department of Agronomy, College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad 500 030, India
K. Bhanu Rekha
Department of Agronomy, College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad 500 030, India
K. S. Sudhakar
Department of Agronomy, College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad 500 030, India
A. Madhavi
AICRP on STCR Scheme, Professor Jayashankar Telangana State Agricultural University, Hyderabad 500 030, India
S. Triveni
Department of Bio-Energy and Microbiology, Professor Jayashankar Telangana State Agricultural University, Hyderabad 500 030, India
P. Rajaiah
AICRP-FIM Scheme, Professor Jayashankar Telangana State Agricultural University, Hyderabad 500 030, India

Abstract


Soil dehydrogenase (14.2, 11.3 mg TPF g–1 day–1), alkaline phosphatase (109.5, 86.7 mg PNP g–1 soil h–1), acid phosphatase (69.7, 51.6 mg PNP g–1 soil h–1) and urease activity (60.4, 39.9 mg NH4 g–1 2 h–1) in chickpea at flowering and harvest with seed rate @ 105 kg ha–1. Among, nutrient management the corresponding activity (14.9, 11.7 mg TPF g–1 day–1), (120.7, 96.7 mg g PNP g–1 soil h–1), (70.5, 52.7 mg g PNP g–1 soil h–1) was higher with 75% RDF + microbial consortia (MC). Contrarily, urease activity (62.3, 38.7 mg g NH4 g–1 2 h–1), soil available soil nitrogen (181.3, 179.0 kg ha–1) and phosphorus (78.3, 76.5 kg ha–1) were higher under 125% RDF + MC. PCA indicated that among nutrient management, first principal component explained 71.37% variability to urease activity, available soil potassium and dehydrogenase activity and second component (22.34%) to available soil phosphorus and nitrogen. Among planting density, first component explained variability (66.87%) to acid phosphatase and second component (32.11%) to available phosphorus

Keywords


Enzyme activity, nutrient management, PCAbiplot, planting density, soil nutrient dynamics.



DOI: https://doi.org/10.18520/cs%2Fv126%2Fi10%2F1273-1279