Showing papers by "Indian Agricultural Statistics Research Institute published in 2012"

Bioefficacy of novel cyanobacteria-amended formulations in suppressing damping off disease in tomato seedlings

Abstract: Biological control of plant pathogens is receiving increasing relevance, as compared to chemical methods, as they are eco-friendly, economical and indirectly improve plant quality and yield attributes. An investigation was undertaken to evaluate the potential of antagonistic cyanobacteria (Anabaena variabilis RPAN59 and A. oscillarioides RPAN69) fortified formulations for suppressing damping off disease in tomato seedlings challenged by the inoculation of a fungal consortium (Pythium debaryanum, Fusarium oxysporum lycopersici, Fusarium moniliforme and Rhizoctonia solani). Treatment with A. variabilis amended formulations recorded significantly higher plant growth parameters, than other treatments, including biological control (Trichoderma formulation) and chemical control (Thiram-Carbendazim). The A. variabilis amended compost-vermiculite and compost formulations exhibited 10–15 % lower disease severity and 40–50 % higher values than chemical and biological control treatments in terms of fresh weight and height of the plants. In future, in depth analyses regarding the mechanism involved in biocontrol by cyanobacteria and evaluation of these formulations under field conditions are proposed to be undertaken.

An Empirical Comparison of Machine Learning Techniques in Predicting the Bug Severity of Open and Closed Source Projects

Abstract: Bug severity is the degree of impact that a defect has on the development or operation of a component or system, and can be classified into different levels based on their impact on the system. Identification of severity level can be useful for bug triager in allocating the bug to the concerned bug fixer. Various researchers have attempted text mining techniques in predicting the severity of bugs, detection of duplicate bug reports and assignment of bugs to suitable fixer for its fix. In this paper, an attempt has been made to compare the performance of different machine learning techniques namely Support vector machine SVM, probability based Naive Bayes NB, Decision Tree based J48 A Java implementation of C4.5, rule based Repeated Incremental Pruning to Produce Error Reduction RIPPER and Random Forests RF learners in predicting the severity level 1 to 5 of a reported bug by analyzing the summary or short description of the bug reports. The bug report data has been taken from NASA's PITS Projects and Issue Tracking System datasets as closed source and components of Eclipse, Mozilla & GNOME datasets as open source projects. The analysis has been carried out in RapidMiner and STATISTICA data mining tools. The authors measured the performance of different machine learning techniques by considering i the value of accuracy and F-Measure for all severity level and ii number of best cases at different threshold level of accuracy and F-Measure.

Small Area Estimation for Zero-Inflated Data

Abstract: The commonly used method of small area estimation (SAE) under a linear mixed model may not be efficient if data contain substantial proportion of zeros than would be expected under standard model assumptions (hereafter zero-inflated data). The authors discuss the SAE for zero-inflated data under a two-part random effects model that account for excess zeros in the data. Empirical results show that proposed method for SAE works well and produces an efficient set of small area estimates. An application to real survey data from the National Sample Survey Office of India demonstrates the satisfactory performance of the method. The authors describe a parametric bootstrap method to estimate the mean squared error (MSE) of the proposed estimator of small areas. The bootstrap estimates of the MSE are compared to the true MSE in simulation study.

Estimating unmet need for contraception by district within Ghana: an application of small-area estimation techniques.

Abstract: The importance of meeting the unmet need for contraception is nowhere more urgent than in the countries of sub-Saharan Africa, where the fertility decline is stalling and total unmet need exceeds 30 per cent among married women. In Ghana, where fertility levels vary considerably, demographic information at sub-national level is essential for building effective family planning programmes. We used small-area estimation techniques, linking data from the 2003 Ghana Demographic and Health Survey to the 2000 Ghana Population and Housing Census, to derive district-level estimates of contraceptive use and unmet need for contraception. The results show considerable variation between districts in contraceptive use and unmet need. The prevalence of contraceptive use varies from 4.1 to 41.7 per cent, while that of the use of modern methods varies from 4.0 to 34.8 per cent. The findings identify districts where family planning programmes need to be strengthened.

Small area estimation of proportions in business surveys

Abstract: Binary data are often of interest in business surveys, particularly when the aim is to characterize grouping in the businesses making up the survey population. When small area estimates are required for such binary data, use of standard estimation methods based on linear mixed models (LMMs) becomes problematic. We explore two model-based techniques of small area estimation for small area proportions, the empirical best predictor (EBP) under a generalized linear mixed model and the model-based direct estimator (MBDE) under a population-level LMM. Our empirical results show that both the MBDE and the EBP perform well. The EBP is a computationally intensive method, whereas the MBDE is easy to implement. In case of model misspecification, the MBDE also appears to be more robust. The mean-squared error (MSE) estimation of MBDE is simple and straightforward, which is in contrast to the complicated MSE estimation for the EBP.

Genetic Variability for Chickpea (Cicer arietinum L.) under Late Sown Season

Abstract: Forty four promising lines of chickpea were grown in RBD with three replications under late sown season. The maximum genotypic coefficient of variation was noticed for damaged pod percentage, total number of seeds per plant and total number of pods per plant. Days to 50% flowering, days to maturity, plant height, 100 seed weight and seed yield per plant showing high heritability coupled with medium genetic advance as percentage of mean, whereas, damage pod percentage, number of seeds per plant and number of pods per plant showing medium heritability and high genetic advance as percentage of mean. Seed yield per plant showed high significant positive correlation with total number of seeds per plant, total number of pods per plant, biological yield, plant height and 100 seed weight, whereas, significant negative correlation with days to 50% flowering and damaged pod percentage. Based on D2 cluster analysis, the forty four genotypes were grouped into nine clusters, depending upon the genetic constitution of the genotypes. The maximum intra cluster distance was found in cluster IV followed by cluster I, cluster VI and cluster VIII. Inter cluster values varied from 2.75 to 9.02. Total pods per plant, 100 seed weight, days to maturity, biological yield and seed yield per plant considered as selection criteria, while selecting superior genotypes under late condition. High yielding advanced breeding lines viz. JG 14, JSC 56, AKG 70, JG 9602974, BG 3005, PG 03110, Phule G 00108 were found suitable under late sown condition.

Seasonal parasitism and biological characteristics of Habrobracon hebetor (Hymenoptera: Braconidae)- a potential larval ectoparasitoid of Helicoverpa armigera (Lepidoptera: Noctuidae) in a chickpea ecosystem

Abstract: Seasonal parasitism of Habrobracon hebetor (Say) on Helicoverpa armigera (Hubner) in chickpea was studied for three consecutive years. Parasitism by H. hebetor on larvae of H. armigera reached 12.3%. The parasitoid maintained reproductive activity on H. armigera from February to April coinciding with pod formation and maturation stages of the crop. In laboratory assays, we investigated the suitability of larval instars of H. armigera to the parasitoid H. hebetor. This parasitoid attacked third to sixth instars, though fourth and fifth instar larvae were found most suitable with 100% parasitism and development to adults. Parasitoid developmental time was longest in fifth instar (9.1 days) compared to other instars (8.1–8.9 days). Fifth instar larvae resulted in highest numbers of cocoons and adult emergence. In addition, suitability of seven lepidopteran species to H. hebetor was investigated. Corcyra cephalonica, Galleria mellonella and H. armigera were the most suitable hosts with 100% parasitism and dev...

Model-based direct estimation of small-area distributions

Abstract: Summary Much of the small-area estimation literature focuses on population totals and means. However, users of survey data are often interested in the finite-population distribution of a survey variable and in the measures (e.g. medians, quartiles, percentiles) that characterize the shape of this distribution at the small-area level. In this paper we propose a model-based direct estimator (MBDE, Chandra and Chambers) of the small-area distribution function. The MBDE is defined as a weighted sum of sample data from the area of interest, with weights derived from the calibrated spline-based estimate of the finite-population distribution function introduced by Harms and Duchesne, under an appropriately specified regression model with random area effects. We also discuss the mean squared error estimation of the MBDE. Monte Carlo simulations based on both simulated and real data sets show that the proposed MBDE and its associated mean squared error estimator perform well when compared with alternative estimators of the area-specific finite-population distribution function.

Trends in the codon usage patterns of Chromohalobacter salexigens genes.

Abstract: Chromohalobacter salexigens, a Gammaproteobacterium belonging to the family Halomonadaceae, shows a broad salinity range for growth In order to reveal the factors influencing architecture of protein coding genes in C salexigens, pattern of synonymous codon usage bias has been investigated Overall codon usage analysis of the microorganism revealed that C and G ending codons are predominantly used in all the genes which are indicative of mutational bias Multivariate statistical analysis showed that the genes are separated along the first major explanatory axis according to their expression levels and their genomic GC content at the synonymous third positions of the codons Both NC plot and correspondence analysis on Relative Synonymous Codon Usage (RSCU) indicates that the variation in codon usage among the genes may be due to mutational bias at the DNA level and natural selection acting at the level of mRNA translation Gene length and the hydrophobicity of the encoded protein also influence the codon usage variation of genes to some extent A comparison of the relative synonymous codon usage between 10% each of highly and lowly expressed genes determines 23 optimal codons, which are statistically over represented in the former group of genes and may provide useful information for salt-stressed gene prediction and gene-transformation Furthermore, genes for regulatory functions; mobile and extrachromosomal element functions; and cell envelope are observed to be highly expressed The study could provide insight into the gene expression response of halophilic bacteria and facilitate establishment of effective strategies to develop salt-tolerant crops of agronomic value

Use of discriminant function analysis for forecasting crop yield

Abstract: The present paper deals with use of discriminant function analysis for developing wheat yield forecast model for Kanpur (India). Discriminant function analysis is a technique of obtaining linear/Quadratic function which discriminates the best among populations and as such, provides qualitative assessment of the probable yield. In this study, quantitative forecasts of yield have been obtained using multiple regression technique taking regressors as weather scores obtained through discriminant function analysis. Time series data of 30 years (1971-2000) have been divided into three categories: congenial, normal and adverse, based on yield distribution. Taking these three groups as three populations, discriminant function analysis has been carried out. Discriminant scores obtained from this have been used as regressors in the modelling. Various strategies of using weekly weather data have been proposed. The models have been used to forecast yield in the subsequent three years 2000-01 to 2002-03 (which were not included in model development). The approach provided reliable yield forecast about two months before harvest.

Disease diagnosis in crops using content based image retrieval

Abstract: Farmers always need satisfactory and easy advice from experts. To get the advice from an expert system, it should have enough knowledge about the domain. Gathering enough knowledge and representing it in a machine understandable format is time consuming and difficult job. Also, representing each and every kind of knowledge is still a research issue. Since, a single picture is worth a thousand words, it will be a good idea to acquire knowledge also in images rather than only text. Image is an easy way of communication without any boundary of language. Hence there is a need for building an expert system with content based image retrieval which could acquire and deliver the knowledge by searching the image having the similar features that is searched by the user. In the presented work, a system is developed to diagnose diseases in crops by matching the uploaded image of a diseased plant from the corpus of images. Three techniques viz. CEDD, Auto Color Correlogram and FCTH are tested and the result is presented. The automatic image based disease diagnosis in crops may help farmers in early detection of diseases and losses due to the image infestation can be reduced.

Computer-Generated Change-Over Designs for Correlated Observations

Abstract: Change-over designs with independently distributed errors in the model have been studied extensively in the literature. Martin and Eccleston (2001) gave an algorithm for the generation of efficient change-over designs when the errors are correlated. This article proposes an algorithm for the generation of efficient change-over designs for estimation of direct effects of treatments in the presence of first-order residual effects in the model and when the errors are correlated.

An Optimal Procedure for Sample Coordination Using Multiple Objective Functions and Nearest Proportional to IPPS Size Sampling Designs

Abstract: The sample coordination problem involves maximization or minimization of overlap of sampling units in different/repeated surveys. Several optimal techniques using transportation theory, controlled rounding, and controlled selection have been suggested in literature to solve the sample coordination problem. In this article, using the multiple objective programming, we propose a method for sample coordination which facilitates variance estimation using the Horvitz–Thompson estimator. The proposed procedure can be applied to any two-sample surveys having identical universe and stratification. Some examples are discussed to demonstrate the utility of the proposed procedure.

Homology Modeling of Buffalo (Bubalus bubalis) Interferon-Tau Protein

Abstract: Interferon-Tau (IFNt) contributes towards maternal recognition of pregnancy in ruminants (like, cattle, buffalo, goat, giraffe). IFNt has been extensively studied in most of the ruminants except for buffalo Bubalus bubalis). The present study has been undertaken to predict the secondary structure of Interferon-tau in buffalo. The available amino acid sequence of bubaline IFNt (sequence database of SwissProt) was subjected to protein-BLAST to find similar sequences with high scores and low e-values. The ovine IFNt sequence (PDB code: 1B5L) was selected for further computational analysis of the bubaline IFNt sequence to predict the secondary and tertiary structure. The secondary structure of the modeled bubaline IFNt was predicted using STRIDE. The 3D structure was generated using academic version of MODELER9v6. The probability density functions (pdf) were used to restrain Cα-Cα distances, main chain N-O distances as well as main-chain and side-chain dihedral angles. The energy minimization and van der waal contacts were taken care of using ACCELRYS DS Modeling 2.0. The residue profiles of the obtained three-dimensional models were checked by VERIFY3D. The energetic architecture and the correctness of the generated model revealed that the predicted secondary model was correct and acceptable. The predicted structure of the bubaline IFNt constructed through homology modeling from ovine IFNt, could be used for further profiling the species specific difference in IFNt activity.

Study of Growth Pattern of Cattle Under Different Climatic Conditions

Abstract: Different sigmoidal nonlinear growth models were fitted in the growth data of triple cross (Friesian×Sahiwal×Hariana) breed and double cross (Friesian×Sahiwal) breed at Agra and Dehradun farms which have different climatic conditions. On the basis of minimum RMSE, the Gompertz model was found to be the best fit. The estimates of growth rates and maturity weights of Friesian×Sahiwal×Hariana breed were considered better under homoscedastic and heteroscedastic error conditions at the Agra farm. Similarly, the growth rates and maturity weights of Friesian×Sahiwal breed were better at the Agra farm, as compared to the Dehradun farm. Therefore, it may be concluded that the climate of Agra (plain area) was more suitable for both the breeds for attaining the maturity weight, as compared to Dehradun (hilly area).

Nonlinear regression techniques for analysis of onion (Allium cepa) production in India

Abstract: India occupies second position in the production of onion in the world. Keeping in view the importance of this crop, the present study has been undertaken to find out the growth in production of onion and discusses the application of nonlinear models, viz. Gompertz, Logistic, MMF, Richards and Weibull models, which measure the growth. Time series data on onion production in major growing states; viz. Andhra Pradesh, Gujarat, Karnataka, Maharashtra, Uttar Pradesh, and all India from 1990–91 to 2009–10 has been utilized for the present study. From a realistic point of view, the relationships among variables in agricultural and horticultural sciences are non-linear in nature. Non-linear models are very popularly used to estimate the trend in various fields such as population studies and animal growth where growth is not symmetrical about the point of inflection. The results showed that Logistic and Gompertz models faired marginally better than Weibull and MMF models

Computational approaches, databases and tools for in silico motif discovery

Abstract: Motifs are the biologically significant fragments of nucleotide or peptide sequences in a specific pattern. Motifs are categorized as structural motifs and sequence motifs. These are discovered by phylogenetic studies of similar genes across species. Structural motifs are formed by three dimensional arrangements of amino acids consisting of two or more α helices or β strands whereas sequence motifs are formed by the nucleotide fragments appearing in the exons of a gene. The arrangement of residues in structural motifs may not be continuous while it is continuous in sequence motifs. Sequence motifs may encode to the structural motifs. The algorithms used for motif discovery are important part of the bio-computational studies. The purpose of motif discovery is to identify patterns in biopolymer (nucleotide or protein) sequences to understand the structure and function of the molecules and their evolutionary aspects. The main aim of this paper is to provide systematic compilation of a review on different approaches, databases and tools used in motif discovery.

A Review of Block Designs for Test Treatments – Control(s) Comparisons

Abstract: In practice there may arise experimental situations where it is desired to compare several treatments called the test treatments to a standard treatment called control. The main interest here lies in making test treatment-control comparison with as much precision as possible and comparison within the test treatments are of less importance. For example in agricultural experiments, the aim of the experimenter is to test a set of new varieties of a crop with an already existing variety and to determine which of the varieties perform better in comparison to the existing variety. Balanced Treatment Incomplete Block (BTIB) designs have been defined for this situation. The designs are balanced with respect to test treatment-control comparisons. The concept of BTIB is further extended to define Balanced Two Disjoint Sets of Treatments (BTDT) designs when there are more than one control. Some methods of constructing these designs are presented here. Some class of row-column designs, which are balanced for test treatments vs. control comparisons, referred to as the Balanced Treatment vs. Control Row- Column (BTCRC) designs are also described when heterogeneity is to be eliminated in two directions. Key words: Balanced Treatment Incomplete Block (BTIB) design; Balanced Two Disjoint Sets of Treatments (BTDT) design; Balanced Treatment vs. Control Row-Column (BTCRC) design. DOI: http://dx.doi.org/10.4038/jfa.v2i1.3939 JFA 2009; 2(1): 22-29

Decision Support System for research project duplication detection

Abstract: Decision Support Systems are usually computer applications along with a human component that can sift through large amounts of data and pick between many choices. Among many challenges in front of research managers, checking of duplication of research efforts is a major challenge. Existing systems do provide simple keyword base search wherein one can find similar projects. These systems do not take care of the semantics or sense of the word into consideration. Also, these systems fail to provide results if synonyms of the keywords are used in other research projects. Moreover, if the keywords are matched and are not used in the same sense, it is very much likely that the projects may not be similar. The presented approach in this work uses WordNet Ontology for finding the semantics of the textual information available within the system. Two algorithms viz. Filtered Set and Preferential Coefficient are presented that also take care the structure of the research project proposal. The algorithms are implemented for the PIMS-ICAR database that contains more than 5000 research project documents.