Journal of Environmental Biology

Impact of organic and mineral inputs onto soil biological and metabolic activities under a long-term rice-wheat cropping system in sub-tropical Indian Inceptisols

Nirmalendu Basak^1,2*, Ashim Datta^1,2, Tarik Mitran², Biswapati Mandal² and P.K. Mani²

¹Division of Soil & Crop Management, ICAR-Central Soil Salinity Research Institute, Karnal 132 001, India

²Directorate of Research, Bidhan Chandra Krishi Viswavidyalaya, Kalyani-741 235, India

*Corresponding Author E-mail: basaknirmalendu@gmail.com

Publication Data

Paper received:

11 May 2014

Revised received:

13 May 2015

Accepted:

04 June 2015

Abstract

Long-term use of organic and mineral inputs has an overriding impact on soil biological and metabolic activities and crop management.? Farm yard manure (FYM), paddy straw (PS) and green manure (GM, Sesbania sesban L.) were used for 24- years old rice (Oryza sativa L.) -wheat (Triticum aestivum L.) cropping system in sub-tropical India to predict whether the screened soil biological and metabolic activities are correlated with system yield. The integrated approaches viz., NPK + FYM, NPK + PS and NPK + GM significantly increased both rice and wheat yield together by 67.5, 44.4 and 55.4%, respectively over control. However, for a few exceptions both soil microbial activity and metabolic activity were remarkably enhanced under integrated treatment NPK + FYM followed by NPK + PS, and NPK + GM, respectively. Among the studied attributes fluorescein diacetate hydrolyzing, dehydrogenase, β-glucosidase activity (β-glu) and microbial biomass C (Cmic) were screened through principal component (PCA) and discriminate analysis (DA) that explained nearly 89% of total variations of the entire data set. Among the four identified attributes, only β-glu assay value could predict system yield (R²= 0.65). Further, estimation of β-glu activity in soil can predict other soil biological properties (R²= 0.96). ?  

 Key words

β-glucosidase activity, Connonical discriminate analysis, Organic input and? Principal component analysis

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