Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a facility organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Small area estimation. The organization has 454 authors who have published 870 publications receiving 7987 citations.

A hybrid spatio-temporal modelling: an application to space-time rainfall forecasting

Abstract: Efficient and reliable forecasting techniques for various climatic conditions are indispensable in agricultural dependent country like India. In this context, rainfall forecasting is one of the most challenging tasks because of the existence of three patterns, viz., temporal, spatial, and non-linear, simultaneously. Space-Time Autoregressive Moving Average (STARMA) model is one of the promising and popular approaches for modelling spatio-temporal time series data. However, the observed features of many space-time rainfall data comprise complex non-linear dynamics and modelling these patterns often go beyond the capability of conventional STARMA model. Moreover, despite the popularity of artificial neural network (ANN) and support vector machine (SVM) for modelling complex non-linear dynamics, they are not capable to deal with spatial patterns. To overcome the problem, a new spatio-temporal hybrid modelling approach has been proposed by integrating STARMA, ANN, and SVM as well. The proposed approach has been empirically illustrated on annual precipitation data of six districts of northern part of West Bengal, India. The study reveals that proposed spatio-temporal hybrid approach has better modelling and forecasting precision over conventional STARMA as well as most widely used Autoregressive Integrated Moving Average (ARIMA) model.

Adsorption–desorption of tricyclazole: effect of soil types and organic matter

Abstract: Adsorption–desorption of tricyclazole was studied by batch equilibrium method in two soil types, varying in their physical and chemical properties. The adsorption of tricyclazole on the soil matrix exhibited low rate of accumulation with 18.24 ± 0.14 % in Ultisol and moderately high rate with 43.62 ± 0.14 % in Vertisol after 6 h of equilibrium time. For soils amended with farmyard manure (FYM), the adsorption percentage increased to 32.52 ± 0.14 % in Ultisol and 55.14 ± 0.14 % in Vertisol. The Freundlich model was used to describe the adsorption–desorption of the tricyclazole in two soils. The adsorption isotherm suggested a relatively higher affinity of tricyclazole to the adsorption sites at low equilibrium concentrations. Variation in sorption affinities of the soils as indicated by the distribution coefficient (K d) for sorption in the range of 0.78 ± 0.01–1.38 ± 0.03, 1.71 ± 0.03–2.99 ± 0.09, 2.75 ± 0.05–4.69 ± 0.01, and 4.65 ± 0.08–7.64 ± 0.01 mL/g for Ultisol, FYM-amended Ultisol, Vertisol, and FYM-amended Vertisol, respectively. Desorption was slower than adsorption, indicating a hysteresis effect. The hysteresis coefficient varied from 0.023 ± 0.15 to 0.160 ± 0.12 in two test soils. A good fit to the linear and Freundlich isotherms was observed with correlation coefficients >0.96. The results revealed that adsorption–desorption was influenced by soil properties and showed that the maximum sorption and minimum desorption of pesticide were observed in soils with higher organic carbon and clay content. Thus, groundwater contamination may be minimized, on application of tricyclazole in high-sorption soils of rice-growing regions.

ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based On g-Spaced Di-peptide Features and Support Vector Machine.

Abstract: Heat shock proteins (HSPs) play a pivotal role in cell growth and variability. Since conventional approaches are expensive and voluminous protein sequence information are available in the post-genomic era, development of an automated and accurate computational tool is highly desirable for prediction of HSPs, their families and sub-types. Thus, we propose a computational approach for reliable prediction of all these components in a single framework and with higher accuracy as well. The proposed approach achieved an overall accuracy of ~84% in predicting HSPs, ~97% in predicting six different families of HSPs and ~94% in predicting four types of DnaJ proteins, with bench mark datasets. The developed approach also achieved higher accuracy as compared to most of the existing approaches. For easy prediction of HSPs by experimental scientists, a user friendly web server ir-HSP is freely accessible at http://cabgrid.res.in:8080/ir-hsp. The ir-HSP was further evaluated for proteome-wide identification of HSPs by using proteome datasets of eight different species, and ~50% of the predicted HSPs in each species were found to be annotated with InterPro HSP families/domains. Thus, the developed computational method is expected to supplement the currently available approaches for HSP prediction to the extent of families and sub-types. Key words: Molecular chaperones, Heat shock, Protein folding, Machine learning, Di-peptide composition, DnaJ proteins

Assessment of Co-Compost Quality by Physico-Chemical and Exploratory Data Analysis

Abstract: The dynamic parameters of the transformation of fresh cow dung (FCD), municipal solid waste (MSW), pond sediment (PST), tea pruning litter (TPL), tea waste (TWE), and water hyacinth (WHH) into a manure using a co-composting process were investigated Among the six different modes of compost, it was observed that the best quality of compost can be produced where the substrate was FCD/MSW/TPL/PST/TWE/WHH 1:15:15:25:25:1 with respect to Indian compost standard Hierarchical agglomerative cluster analysis (HCA) for physical and chemical variables during composting yielded a dendrogram and formed two clusters, one of which includes temperature, amount of cadmium, chromium, copper, lead, MSW, nickel, phosphorus, and zinc and the other includes cation exchange capacity, FCD, germination index of chickpea, germination index of green gram, mercury, nitrogen, organic carbon (OC), pH, TPL, potassium, PST, TWE, and WHH Principal component analysis (PCA) was applied to all the data sets, which resulted in nine, four, four, three, and two latent factors of the total variance in compost quality Varifactors of PCA implied that the parameters responsible for metals and P were MSW and temperature variation, N was mainly related to PST and TWE whereas OC was influenced by TPL and FCD Therefore, on application of HCA and PCA, a meaningful classification of the above-mentioned parameters has been obtained Thus, these results should be effective measures for future in using tea garden waste materials for the preparation of valued eco-friendly compost