Kumar N.,Indian Agricultural Research Institute |
Mukherjee I.,Indian Agricultural Research Institute |
Sarkar B.,Indian Agricultural Research Institute |
Journal of Hazardous Materials | Year: 2017
Pesticide persistence and degradation in soil are influenced by factors like soil characteristics, light, moisture etc. Persistence of tricyclazole was studied under different soil moisture regimes viz., dry, field capacity and submerged in two different soil types viz., Inceptisol and Ultisol from Delhi and Karnataka, respectively. Tricyclazole dissipated faster in submerged (t1/2 160.22–177.05 d) followed by field capacity (t1/2 167.17–188.07 d) and dry (t1/2 300.91–334.35 d) in both the soil types. Half-life of tricyclazole in Delhi field capacity soil amended with Blue Green Algae (BGA), was 150.5 d as compared to 167.1 d in unamended soil. In Karnataka soil amended with BGA the half-lives were 177.0 d compared to 188.0 d in unamended soil, indicating that BGA amendment enhanced the rate of dissipation of in both the selected soils. Tricyclazole was found to be stable in water over a pH range of 3–9, the half life in paddy field was 60.20 d and 5.47 d in paddy soil and paddy water, respectively. Statistical analysis and Duncan's Multiple Range Test (DMRT) revealed significant effect of moisture regime, organic matter and atmospheric CO2 level on dissipation of tricyclazole from soil and pH of water (at 95% confidence level p < 0.0001). © 2016 Elsevier B.V.
Singh P.,NDRI |
Indian Journal of Animal Sciences | Year: 2014
Information regarding the fertility index in relation to sperm attributes, which helps in the selection of future breeding bulls are meager in buffaloes. The present study was conducted to measure the differences in motility characteristics, head biometry, acrosome, plasma membrane and DNA of cryopreserved semen of fertile and subfertile buffalo bulls. The fertility of bulls was classified on the basis of conception rates (CR), where bulls having CR 28-35% and >55% were considered as sub-fertile and fertile bulls respectively. Computer assisted semen analyzer was used for motility and viability studies. Total motility, average path velocity (VAP), straight linear velocity (VSL) and curvilinear velocity (VCL) of sperm for fertile bulls were significantly higher than sub-fertile bulls. Significant differences were found in the length and width of sperm head between the 2 groups. The percentage of intactness of sperm acrosome of fertile bulls was significantly higher than sub-fertile bulls. The percentage of apoptotic sperm differed significantly between fertile and sub-fertile bulls. The sperm DNA integrity of fertile and sub-fertile bulls was not significantly different. In conclusion, the total motility, VAP, VCL, VSL, length and width of sperm head, acrosome integrity and percentage of apoptotic sperm, are useful for evaluating bulls' semen quality to reduce the risk of using semen of poor-fertility bulls in AI programme.
Das S.,I.A.R.I |
Dahiya S.,IASRI |
2014 International Conference on Computing for Sustainable Global Development, INDIACom 2014 | Year: 2014
Classification is an important and widely carried out task of data mining. It is a predictive modelling task which is defined as building a model for the target variable as a function of the explanatory variables. There are many well established techniques for classification, while decision tree is a very important and popular technique from the machine learning domain. Decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs and utility. C4.5 is a well known decision tree algorithm used for classifying datasets. The C4.5 algorithm is Quintan's extension of his own ID3 algorithm for decision tree classification. It induces decision trees and generates rules from datasets, which could contain categorical and/or numerical attributes. The rules could be used to predict categorical values of attributes from new records. C4.5 performs well in classifying the dataset as well as in generating useful rules. In this paper, a web based software for rule generation and decision tree induction using C4.5 algorithm has been discussed. The visualization in the form of tree structure enhances the understanding of the generated rules. The software contains the feature to impute the missing values in data. The input data can both be categorical and numerical in nature. The software can import TXT, XLS and CSV data file formats. Enhanced waterfall model has been used for the software development process. This software will be useful for academicians, researchers and students working in the area of data mining, agriculture and other fields where huge amount of data is generated. © 2014 IEEE.
Abedinpour M.,Indian Agricultural Research Institute |
Sarangi A.,Indian Agricultural Research Institute |
Rajput T.B.S.,Indian Agricultural Research Institute |
Singh M.,Indian Agricultural Research Institute |
And 2 more authors.
Agricultural Water Management | Year: 2012
Crop growth simulation models of varying complexity have been developed for predicting the effects of soil, water and nutrients on grain and biomass yields and water productivity of different crops. These models are calibrated and validated for a given region using the data generated from field experiments. In this study, a water-driven crop model AquaCrop, developed by FAO was calibrated and validated for maize crop under varying irrigation and nitrogen regimes. The experiment was conducted at the research farm of the Water Technology Centre, IARI, New Delhi during kharif 2009 and 2010. Calibration was done using the data of 2009 and validation with the data of 2010. Irrigation applications comprised rainfed, i.e. no irrigation (W 1) irrigation at 50% of field capacity (FC) (W 2) at 75% FC (W 3) and full irrigation (W 4). Nitrogen application levels were no nitrogen (N 1), 75kgha -1 (N 2) and 150kgha -1 (N 3). Model efficiency (E), coefficient of determination (R 2), Root Mean Square error (RMSE) and Mean Absolute Error (MAE) were used to test the model performance. The model was calibrated for simulating maize grain and biomass yield for all treatment levels with the prediction error statistics 0.95
Pandey R.K.,Amity University |
Singh S.R.,Indian Agricultural Research Institute |
Gupta P.K.,Indian Agricultural Research Institute |
Goswami B.K.,Amity University |
And 2 more authors.
Indian Journal of Agricultural Sciences | Year: 2011
Biocontrol potential of the parasitic fungus Paecilomyces lilacinus (Pl-181) strain was isolated from the egg masses of the nematode affected tomato (Solanum esculentum L.; syn Lycopersium esculentum Mill.) crops was evaluated for the management of root-knot nematode (Meloidogyne incognita Chitwood). The fungal isolate grown on starch rich grains was tested for an ideal formulation with different natural and synthetic inert carrier material. Keeping in view the parameters like colony count, shelf-life coupled with easy dispersibility out of Attapulgite-based clay dust powder +peat powder, heavy loam soil powder + peat powder, talc fine powder + peat powder, kaolin light powder + peat powder, boric acid powder + peat powder, bentonite powder + peat powder and Paecilomyces lilacinus + alone were tried separately. Attapulgite-based clay dust powder + peat powder + Acacia gum powder showed best performance in respect to all the three parameters and also suppressed M. incognita population to a greater extent.
Deb C.K.,IASRI |
Marwaha S.,IASRI |
Malhotra P.K.,IASRI |
Wahi S.D.,IASRI |
Pandey R.N.,Indian Agricultural Research Institute
2015 International Conference on Computing for Sustainable Global Development, INDIACom 2015 | Year: 2015
Software's using ontology as their knowledge base are of due importance now a days due to their synergism with agents and Semantic Web Architecture. Ontologies provide domain language by defining domain concepts and relationships between them which is ultimately meaningful to both humans and machines. This is IEEE standard Web Ontology Language (OWL). Taxonomies are well-defined hierarchy existing in a standardized form to describe real world concepts in various domains of knowledge. The indispensable role of ontology in Agriculture is to convert the unstructured knowledge into structured one, sharing across application. Das (2010) and Das et al. (2012) developed Soil Ontology for USDA soil taxonomy for orders available in India to only Sub group level. This newly developed Soil Ontology has been strengthened and is now available up to family and series level for orders in India and also for the twelve orders worldwide. The web based application follows N-tier architecture. By mentioning the soil properties one can easily get information related to soil taxonomy and also newly found soils can be classified. Information edition or addition facilities of soil taxonomy are available with domain experts. Advance Search and series navigation keys can be use to easily get the detailed information of taxonomic hierarchy and state wise series description respectively. Its knowledge base is in the form of Ontology. © 2015 IEEE.
PubMed | IASRI and Indian Agricultural Research Institute
Type: | Journal: Journal of hazardous materials | Year: 2016
Pesticide persistence and degradation in soil are influenced by factors like soil characteristics, light, moisture etc. Persistence of tricyclazole was studied under different soil moisture regimes viz., dry, field capacity and submerged in two different soil types viz., Inceptisol and Ultisol from Delhi and Karnataka, respectively. Tricyclazole dissipated faster in submerged (t1/2 160.22-177.05d) followed by field capacity (t1/2 167.17-188.07d) and dry (t1/2 300.91-334.35d) in both the soil types. Half-life of tricyclazole in Delhi field capacity soil amended with Blue Green Algae (BGA), was 150.5d as compared to 167.1d in unamended soil. In Karnataka soil amended with BGA the half-lives were 177.0d compared to 188.0d in unamended soil, indicating that BGA amendment enhanced the rate of dissipation of in both the selected soils. Tricyclazole was found to be stable in water over a pH range of 3-9, the half life in paddy field was 60.20d and 5.47d in paddy soil and paddy water, respectively. Statistical analysis and Duncans Multiple Range Test (DMRT) revealed significant effect of moisture regime, organic matter and atmospheric CO2 level on dissipation of tricyclazole from soil and pH of water (at 95% confidence level p<0.0001).
Ahmad T.,I.A.S.R.I. |
Sahoo P.M.,I.A.S.R.I. |
International Journal of Agricultural and Statistical Sciences | Year: 2015
Agroforestry plays a vital role in achieving integrated rural and urban development. But any standard statistical methodology for estimation of area under agroforestry to provide reliable data on agroforestry area is not available. In this paper, we propose to estimate area under agroforestry using remote sensing techniques in Vaishali district of Bihar State. Land use/land cover map of the district has also been generated using ERDAS IMAGINE software. It has been observed that the estimate of area under agroforestry obtained using high resolution satellite data under this study is reliable.
Molecular Breeding | Year: 2010
Most characters of economic importance in plants and animals, and complex diseases in humans, exhibit quantitative variation, the genetics of which has been a fascinating subject of study since Mendel's discovery of the laws of inheritance. The classical genetic basis of continuous variation based on the infinitesimal model of Fisher and mostly using statistical methods has since undergone major modifications. The advent of molecular markers and their extensive mapping in several species has enabled detection of genes of metric characters known as quantitative trait loci (QTL). Modeling the high-resolution mapping of QTL by association analysis at the population level as well as at the family level has indicated that incorporation of a haplotype of a pair of single-nucleotide polymorphisms (SNPs) in the model is statistically more powerful than a single marker approach. High-throughput genotyping technology coupled with micro-arrays has allowed expression of thousand of genes with known positions in the genome and has provided an intermediate step with mRNA abundance as a sub-phenotype in the mapping of genotype onto phenotype for quantitative traits. Such gene expression profiling has been combined with linkage analysis in what is known as eQTL mapping. The first study of this kind was on budding yeast. The associated genetic basis of protein abundance using mass spectrometry has also been attempted in the same population of yeast. A comparative picture of transcript vs. protein abundance levels indicates that functionally important changes in the levels of the former are not necessarily reflected in changes in the levels of the latter. Genes and proteins must therefore be considered simultaneously to unravel the complex molecular circuitry that operates within a cell. One has to take a global perspective on life processes instead of individual components of the system. The network approach connecting data on genes, transcripts, proteins, metabolites etc. indicates the emergence of a systems quantitative genetics. It seems that the interplay of the genotype-phenotype relationship for quantitative variation is not only complex but also requires a dialectical approach for its understanding in which 'parts' and 'whole' evolve as a consequence of their relationship and the relationship itself evolves. © 2010 Springer Science+Business Media B.V.
Ahuja S.,IASRI |
2015 International Conference on Computing for Sustainable Global Development, INDIACom 2015 | Year: 2015
Agroforestry describes the land use management system in which trees or shrubs are grown around or among crops or pastureland. It combines agricultural and forestry technologies to create more diverse, productive, profitable, healthy, and sustainable land-use systems. The treatment combinations of doses, fertilizers, variety of crops and their spacing i. e. geometrical arrangements, canopy manipulations, crop harvest intervals, irrigation schedules etc. are standardized and judged specifically to develop different Agriculture and Forestry Models. Cluster ensemble technique has been proved to be better than any of the traditional clustering algorithms fordiscovering complicated structures in data. Cluster ensembles can provide robust and stable solutions by leveraging the consensusacross multiple clustering results, while averaging out emergent spurious structures that arisedue to the various biases to which each participating algorithm is tuned. In this paper, a cluster ensemble technique for Optimum Growth Ensemble in Agroforestry (OGEA) has been proposed. OGEAaims at improving robustnessand quality of clustering scheme, particularly in Agroforestry sector which in turn enhance the production and productivity of any crop. OGEA consists of four phases. First phase generates the various clustering schemes. This phase does the relabeling to avoid the label correspondence problem. The second phase predicts the tuples by using the three different techniques of prediction viz., Discriminant Analysis, Multilayer perceptron and Logistic regression. In the phase III, depending upon the results of the best technique and threshold of the consensus function obtained by various clustering schemes, consensus partition is generated. In the phase IV, Performance Groups are determined in descending order of optimum resultsi. e. Performance Group 1 gives the maximum yield or survival percentage followed by other Performance Groups respectively. Extensive experimentation has been done on the data setby varying the number of partitions and clusters in cluster ensemble. Different Performance Groups are achieved by using this technique that segregates the various treatment combinations in order to achieve the optimum production. Furthermore, we investigate in depth the about the quality, accuracy and stability of results by using different Performance Groups by utilizing the various quality measures viz., Purity, Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI). Further, the result is statistically tested by determining the comparison of each Performance Group with Control by using the various statistical measures such as Mean, Standard Deviation and Coefficient of Variation. © 2015 IEEE.