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.

Improving soil microbiology under rice-wheat crop rotation in Indo-Gangetic Plains by optimized resource management.

Abstract: The resource-intensive agriculture involving use of chemical fertilizers, irrigation, and tillage practices is a major cause of soil, water, and air pollution. This study was conducted to determine whether integrated use of nutrient, water, and tillage (reduced) can be manipulated to improve the population of plant growth promoting rhizobacteria (Azotobacter, Bacillus, and Pseudomonas) to enhance soil fertility and yield. The study was conducted in the Indo-Gangetic plain (IGP) region of India, where resource-intensive agriculture is practiced. Various combinations of chemical (urea) and organic fertilizers (farmyard manure (FYM), biofertilizer, and green manure) were used on replicated field plots for all the experiments. The effect of integrated resource management (IRM) on activities of Azotobacter, Bacillus, and Pseudomonas and its relation to the yields of rice and wheat crops in subtropical soils of IGP region were also observed. The increased population of all the three microbes, namely, Azotobacter (5.01–7.74 %), Bacillus (3.37–6.79 %), and Pseudomonas (5.21–7.09 %), was observed due to improved structure and increased organic matter in the soil. Similarly, kernel number and 1000 kernel weight were found increased with sole organic N source, three irrigations, and conservation tillage. Thus, it was found that the IRM practices affect the environment positively by increasing the population of beneficial soil microbes and crop yield as compared to high-input agriculture (conventional practices).

Project information and management system of ICAR (PIMS-ICAR)

Abstract: Project Information& Management System of ICAR (PIMS-ICAR) has been designed and developed at IASRI, New Delhi to help in taking decisions to check duplication in research projects both at divisional as well as inter divisional level for on-line monitoring and concurrent evaluation of the ongoing research projects and for other management requirements. The Ongoing Projects options module is meant for entering and editing of Project Basic Data and Project Components. Project Basic Data sub-module provide facility to data entry for : Project General Information; Project Specific Area; Collaborating Institute; Institute - Division; Project Documents; Objectives; Activities; Project Budget and Project Expert.

Efficient block designs for symmetric parallel line assays

Abstract: Three methods of constructions of efficient block designs for symmetric parallel line assays have been proposed. These methods are based on balanced incomplete block design (BIBD). Through these designs, all contrasts of interest can be estimated free from block effect and with high efficiency. Any BIB design for which v, the number treatments is strictly greater than twice of block size can be converted into a design that can be used for conducting a symmetric parallel line assay. All methods of construction are demonstrated with some examples.

Molecular Approaches for Identification of Lactobacilli from Traditional Dairy Products

Abstract: Traditional dairy food products harbour groups of several bacterial genera especially lactic acid bacteria (LAB) which play a role essentially in fermentation of these products and provide a unique flavour and identity to the product. Historically, many species of LAB have been considered as ‘generally regarded as safe’ (GRAS) bacteria and are associated with human foods. The GRAS status forms a base for increasing use of LAB in traditional foods and in expanding unique foods and food products that are formulated to have specific nutritional or additional health-enhancing benefits. Lactobacillus is considered as one of the most important genera among LAB. The genera have more than 180 species. Classical microbiological methods have been found insufficient to classify this huge diverse genus. Hence, a better approach is in demand to characterize new strains of lactobacilli. By using modern molecular methods, it has been possible to characterize the lactobacillus associated with traditional dairy products.

Discriminant analysis-based cluster ensemble

Abstract: The problem of instability and non-robustness in K-means clustering has been recognised as a serious problem in both scientific and business applications. Further, these problems get accentuated in the presence of outliers in data. Cluster ensemble technique has been recently developed to combat such problems and improve overall quality of clustering scheme. In this paper, we propose a cluster ensemble method based on discriminant analysis to obtain robust clustering and report noise to the user. Clustering schemes are generated by the partitional clustering algorithm (K-means) for constructing the ensemble. The proposed algorithm operates in three phases. During the first phase, input clustering schemes are reconciled by relabeling the clusters corresponding to an arbitrary reference scheme. This is accomplished using Hungarian algorithm, which is a well-known optimisation approach. The second phase uses discriminant analysis and constructs a label matrix that is used for generating consensus partition. In the final stage, clustering scheme is refined to deliver robust and stable clustering scheme. Empirical evaluation of the algorithm shows that the proposed method significantly improves the quality of resultant ensemble. Further, comparison with the cluster ensembles generated by package R for 20 public datasets demonstrated improved quality of ensembles generated by the proposed algorithm.