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Rice‐based multiple cropping systems are predominant in the Indo‐Gangetic Plains of Indian subcontinent. A decline in yield of such systems has been observed and ascribed to quantitative and qualitative variations of soil organic matter (SOM). We evaluated the impact of the annual rotation: rice (Oryza sativa L.), wheat (Triticum aestivum L.), jute (Corchorus olitorius L.), with and without fertilizer treatments (control, N, N–P, N–P–K, and N–P–K plus farmyard manure [FYM]) on SOM and aggregate properties. At 0‐ to 15‐cm soil depth, microbial biomass C and N, hot water–soluble C and N and hydrolyzable carbohydrates, and particulate organic matter C (POMC) and N (POMN) were found in the order N–P–K plus FYM > N–P–K > N–P > N > control. Over the course of the experiment, application of N alone decreased total organic C (TOC) by 20.4%, whereas N–P–K with or without FYM addition either maintained or enhanced compared to initial. Total soil N and mineralizable N declined in all the treatments except N–P–K plus FYM. Irrespective of treatments, microaggregates (53–250 μm) dominated with 43.9 to 51.3% of total soil aggregate size distribution, followed by macroaggregates (250–2000 μm with 34.6 to 40.1%). The C and N mineralization rate was greater in macroaggregates than in microaggregates, and correlated significantly with POMC (r = 0.67, P ≤ 0.01) and POMN (r = 0.88, P ≤ 0.01). Nitrogen–phosphorus–potassium plus FYM also improved overall soil aggregation as compared to other treatments. Therefore, the results suggest that the gradual depletion of nutrients and structural degradation may have collectively contributed to the crop yield declines in the rice–wheat–jute rotation and that the integrated use of N–P–K and FYM is an important nutrient management option for sustaining this cropping system.

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Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract. We discuss the physics of sub-Doppler DAVLL spectroscopy, which employs a pump beams with an axial magnetic field to induce dichroism in an atomic vapour. The dichroism is measured by a counterpropagating probe beam, while the pump generates sub-Doppler spectral features. The magnitude of the field is chosen to shift the frequency of the absorption features by an amount comparable to their linewidth. The reference signals obtained are ideal for laser frequency discriminant signals (laser 'locking' to the atomic transition) without frequency modulation. We discuss the sensitivity of the spectra to magnetic field, laser power, and polarization purity, and suggest operating parameters for the 87 Rb F = 2 → F = 3, 2 crossover transition which maximise the signal amplitude and gradient.

Bacterial communities in buffalo rumen were characterized using a culture-independent approach for a pooled sample of rumen fluid from 3 adult Surti buffaloes. Buffalo rumen is likely to include species of various bacterial phyla, so 16S rDNA sequences were amplified and cloned from the sample. A total of 191 clones were sequenced and similarities to known 16S rDNA sequences were examined. About 62.82% sequences (120 clones) had >90% similarity to the 16S rDNA database sequences. Furthermore, about 34.03% of the sequences (65 clones) were 85-89% similar to 16S rDNA database sequences. For the remaining 3.14%; the similarity was lower than 85% Phylogenetic analyses were also used to infer the makeup of bacterial communities in the rumen of Surti buffalo. As a result, we distinguished 42 operational taxonomic units (OTUs) based on unique 16S r DNA sequences: 19 OTUs affiliated to an unidentified group (45.23% of total OTUs), 11 OTUs of the phylum Firmicutes, also known as the low G+C group (26.19%), 7 OTUs of the Cytophaga-Flexibacter-Bacteroides phylum (16.66%), 4 OTUs of Spirochaetes (9.52%), and 1 OTU of Actinobacteria (2.38%). These include 10 single-clone OTUs, so Good's coverage (94.76%) of 16S rRNA libraries indicated that sequences identified in the libraries represent the majority of bacterial diversity present in rumen.

The diversity and density of methanogenic archaea and methane production were investigated ex situ at different growth stages of rice plant cultivated in compost-treated tropical rice fields. The qPCR analysis revealed variation in methanogens population from 3.40 × 10(6) to 1.11 × 10(7)  copies g(-1)  dws, in the year 2009 and 4.37 × 10(6) to 1.36 × 10(7)  copies g(-1)  dws in the year 2010. Apart from methanogens, a large number of bacterial (9.60 × 10(9) -1.44 × 10(10)  copies g(-1)  dws) and archaeal (7.13 × 10(7) -3.02 × 10(8)  copies g(-1)  dws) communities were also associated with methanogenesis. Methanogen population size varied in the order: flowering > ripening > tillering > postharvest > preplantation stage. The RFLP-based 16S rRNA gene-targeted phylogenetic analysis showed that clones were closely related to diverse group of methanogens comprising members of Methanomicrobiaceae, Methanosarcinaceae, Methanosaetaceae and RC I. Laboratory incubation studies revealed higher amount of cumulative CH(4) at the flowering stage. The integration of methanogenic community structure and CH(4) production potential of soil resulted in a better understanding of the dynamics of CH(4) production in organically treated rice-field soil. The hypothesis that the stages of plant development influence the methanogenic community structure leading to temporal variation in the CH(4) production has been successfully tested.

Nutrient deficiencies in soil–crop contexts and inappropriate managements are the important reasons for low crop productivity, reduced nutritional quality of agricultural produce and animal/human malnutrition, across the world. The present investigation was carried out to evaluate nutrient deficiencies of sulphur (S) and micronutrients [zinc (Zn), boron (B), iron (Fe), copper (Cu) and manganese (Mn)] in agricultural soils of India for devising effective management strategies to achieve sustainable crop production, improved nutritional quality in crops and better animal/human health. A total of 2,42,827 surface (0–15 cm depth) soil samples were collected from agriculture fields of 615 districts lying in 28 states of India and were analysed for available S and micronutrients concentration. The study was carried out under the aegis of All India Coordinated Research Project on Micro- and Secondary-Nutrients and Pollutant Elements in Soils and Plants. The mean concentrations were 27.0 ± 29.9 mg kg−1 for available S, 1.40 ± 1.60 mg kg−1 for available Zn and 1.40 ± 4.70 mg kg−1 for available B, 31.0 ± 52.2 mg kg−1 for available Fe, 2.30 ± 3.50 mg kg−1 for available Cu and 17.5 ± 21.4 mg kg−1 for available Mn. There were variable and widespread deficiencies of S and micronutrients in different states. The deficiencies (acute deficient + deficient + latent deficiency) of S (58.6% of soils), Zn (51.2% of soils) and B (44.7% of soils) were higher compared to the deficiencies of Fe (19.2% of soils), Cu (11.4% of soils) and Mn (17.4% of soils). Out of 615 districts, > 50% of soils in 101, 131 and 86 districts were deficient in available S, available Zn and available B, respectively. Whereas, > 25% of soils in 83, 5 and 41 districts had deficiencies of available Fe, available Cu and available Mn, respectively. There were occurrences of 2-nutrients deficiencies such S + Zn (9.30% of soils), Zn + B (8.70% of soils), S + B (7.00% of soils) and Zn + Fe (5.80% of soils) to a greater extent compared to the deficiencies of Zn + Mn (3.40% of soils), S + Fe (3.30% of soils), Zn + Cu (2.80% of soils) and Fe + B (2.70% of soils). Relatively lower % of soils were deficient in 3-nutrients (namely S + Zn + B, S + Zn + B and Zn + Fe + B), 4-nutrients (namely Zn + Fe + Cu + Mn) and 5-nutrients (namely Zn + Fe + Cu + Mn + B) simultaneously. The information regarding the distribution of deficiencies of S and micronutrients (both single and multi-nutrients) could be used by various stakeholders for production, supply and application of right kind of fertilizers in different districts, states and agro-ecological regions of India for better crop production, crop nutritional quality, nutrient use efficiency, soil health and for tackling human and animal malnutrition.