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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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The study of growth of Lactococcus lactis NCIM 2114, a nisin producer, was modeled using continuously generated concentration data for growth in fermenter. The sigmoidal growth functions, Logistic, Gompertz, and Richards were used to fit the data. A nonlinear regression method was used to fit the data and estimate growth parameter values of L. lactis, using Marquardt algorithm with Statistical Software SPSS, version 20. Bacterial growth data from the exponential phase of the bacteria's growth was analyzed. An F test showed that the Gompertz and Logistic functions were acceptable 92% and 67% of times respectively in the batch fermenter runs where this particular application was used to derive the lag time, growth rates, and time to maximum growth rates of L. lactis. The maximal specific growth rate ranged between 0.23 h −1 to 0.30 h −1 and the lag time lasted up to a maximum of 1.63 h depending upon aeration conditions provided to the organism. This study will help to estimate specific growth rates and lag time of L. lactis under different growth conditions. Predicted values can be accurately determined.

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