Showing papers by "Indian Agricultural Statistics Research Institute published in 2014"

Outlier Robust Small Area Estimation

Abstract: Recently proposed outlier robust small area estimators can be substantially biased when outliers are drawn from a distribution that has a different mean from that of the rest of the survey data. This naturally leads down to the idea of an outlier robust bias correction for these estimators. In this paper we develop this idea and also propose two different analytical mean squared error estimators for the ensuring bias corrected outlier robust estimators. Simulations based on realistic outlier contaminated data show that the proposed bias correction often leads to more efficient estimators. Furthermore the proposed mean squared error estimators appear to perform well with a variety of outlier robust smal area estimators.

Outlier robust small area estimation

Abstract: Recently proposed outlier robust small area estimators can be substantially biased when outliers are drawn from a distribution that has a different mean from that of the rest of the survey data. This naturally leads down to the idea of an outlier robust bias correction for these estimators. In this paper we develop this idea and also propose two different analytical mean squared error estimators for the ensuring bias corrected outlier robust estimators. Simulations based on realistic outlier contaminated data show that the proposed bias correction often leads to more efficient estimators. Furthermore the proposed mean squared error estimators appear to perform well with a variety of outlier robust smal area estimators.

pH-Sensitive crosslinked guar gum-based superabsorbent hydrogels: Swelling response in simulated environments and water retention behavior in plant growth media

Abstract: Crosslinked guar gum-g-polyacrylate (cl-GG-g-PA) superabsorbent hydrogels were prepared to explore their potential as soil conditioners and carriers. The hydrogels were prepared by in situ grafting polymerization and crosslinking of acrylamide onto a natural GG followed by hydrolysis. Microwave-initiated synthesis under the chosen experimental conditions did not exhibit any significant improvement over the conventional technique. The optimization studies of various synthesis parameters, namely, monomer concentration, crosslinker concentration, initiator concentration, quantity of water per unit reaction mass, particle size of backbone, and concentration of alkali were performed. The hydrogels were characterized by wide-angle X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, and solid-state 13C-NMR spectroscopy. Swelling behavior of a candidate hydrogel [GG-superabsorbent polymer (SAP)] in response to external stimuli, namely, salt solutions, fertilizer solutions, temperature, and pH, was studied. The GG-SAP exhibited significant swelling in various environments. The effect of GG-SAP on water absorption and the retention characteristics of sandy loam soil and soil-less medium were also studied as a function of temperature and moisture tensions. The addition of GG-SAP significantly improved the moisture characteristics of plant growth media (both soil and soil-less), showing that it has tremendous potential for diverse applications in moisture stress agriculture.

Simultaneous removal of multiple pesticides from water: Effect of organically modified clays as coagulant aid and adsorbent in coagulation–flocculation process

Abstract: Contamination of drinking water sources with agrochemical residues became a major concern in the twenty-first century. Coagulation–flocculation is the most widely used water-treatment process, but the efficiency to remove pesticides and other organic pollutants are limited compared to adsorption process. Thus, simultaneous action of adsorption on normal bentonite or organo-modified montmorillonite clays [modified with octadecylamine (ODA-M) and octadecylamine + aminopropyltriethoxysilane (ODAAPS-M)] followed by coagulation–flocculation by alum and poly aluminium chloride has been evaluated for removal of 10 different pesticides, namely atrazine, lindane, metribuzin, aldrin, chlorpyriphos, pendimethalin, α-endosulphan, β-endosulphan, p, p′-DDT, cypermethrin and two of its metabolites, endosulphan sulphate and p, p′-DDE, from water. The coagulation without integration of adsorption was less effective (removal % varies from 12 to 49) than the adsorption–coagulation integrated system (removal % varies from 71...

Predicting the complexity of code changes using entropy based measures

Abstract: Changes in software source codes are inevitable. The source codes of software are frequently changed to meet the user’s enormous requirements. These changes are occurring due to bug repairs (BR), enhancement/modification (EM) and the addition of new features (NF). The maintenance task becomes quite difficult if these changes are not properly recorded. The versions of these frequent changes are being maintained using the software configuration management repository. These continuous changes in the software source code make the code complex and negatively affect the quality of the product. In the literature, the complexity of the code changes has been quantified using entropy based measures (Hassan, in: Proceedings of the 31st international conference on software engineering, pp. 78–88, 2009). In this paper, we have proposed a model to predict the potential complexity of code changes using entropy based measures. The predicted potential complexity of code changes helps in determining the remaining code changes yet to be diffused in the software. The proposed model has been validated using seven components of web browser Mozilla. The model has been evaluated using goodness of fit criteria namely R squared, bias, mean squared error, variation, and root mean squared prediction error (RMSPE).The statistical significance of the proposed model has been tested using χ2 and Kolmogorov–Smirnov (K–S) test. The proposed model is found statistically significant based on the associated p value of the K–S test. Further, we conclude that the rate of complexity diffusion due to BR is found higher in four cases namely Bonsai, Mozbot, tables and XUL. The other components of Mozilla namely AUS, MXR and Tinderbox show increase in complexity due to EM and NF.

Antioxidant activity and phenolic content of the Indian wheat varieties

Abstract: In this study, Indian wheat varieties grown under different agro-climatic zones were evaluated for their antioxidant potential. Different grain fractions (bran, flour, shorts) and the whole meal were tested using two free radicals (ABTS and DPPH) for their radical scavenging activities. More variation was observed in the antioxidant activities from different zones using DPPH assay. Irrespective of the method used, the whole meal and the bran of central zone varieties showed the highest and the north western plains zone varieties showed the lowest antioxidant activities. Within each growing zone, both the effect of genotype and environment was observed on the antioxidant activity. Both free and bound phenolic compounds were extracted from the bran of varieties representing different zones. Total phenolic content (TPC) varied from 2,900 to 5,650 μg Gallic acid equivalents/g of bran. Bound phenolic content was found to be more strongly correlated to the TPC than the free phenolic content. Highly significant genotypic differences were observed in the total phenolic content. This study therefore indicates that Indian wheat varieties have good antioxidant activity and high content of phenolic compounds and can be used further in breeding programmes to increase the content of phytochemicals responsible for antioxidant activity.

The Halophile protein database.

Abstract: Halophilic archaea/bacteria adapt to different salt concentration, namely extreme, moderate and low. These type of adaptations may occur as a result of modification of protein structure and other changes in different cell organelles. Thus proteins may play an important role in the adaptation of halophilic archaea/bacteria to saline conditions. The Halophile protein database (HProtDB) is a systematic attempt to document the biochemical and biophysical properties of proteins from halophilic archaea/bacteria which may be involved in adaptation of these organisms to saline conditions. In this database, various physicochemical properties such as molecular weight, theoretical pI, amino acid composition, atomic composition, estimated half-life, instability index, aliphatic index and grand average of hydropathicity (Gravy) have been listed. These physicochemical properties play an important role in identifying the protein structure, bonding pattern and function of the specific proteins. This database is comprehensive, manually curated, non-redundant catalogue of proteins. The database currently contains 59 897 proteins properties extracted from 21 different strains of halophilic archaea/bacteria. The database can be accessed through link. Database URL: http://webapp.cabgrid.res.in/protein/

A statistical approach for 5′ splice site prediction using short sequence motifs and without encoding sequence data

Abstract: Background: Most of the approaches for splice site prediction are based on machine learning techniques. Though, these approaches provide high prediction accuracy, the window lengths used are longer in size. Hence, these approaches may not be suitable to predict the novel splice variants using the short sequence reads generated from next generation sequencing technologies. Further, machine learning techniques require numerically encoded data and produce different accuracy with different encoding procedures. Therefore, splice site prediction with short sequence motifs and without encoding sequence data became a motivation for the present study. Results: An approach for finding association among nucleotide bases in the splice site motifs is developed and used further to determine the appropriate window size. Besides, an approach for prediction of donor splice sites using sum of absolute error criterion has also been proposed. The proposed approach has been compared with commonly used approaches i.e., Maximum Entropy Modeling (MEM), Maximal Dependency Decomposition (MDD), Weighted Matrix Method (WMM) and Markov Model of first order (MM1) and was found to perform equally with MEM and MDD and better than WMM and MM1 in terms of prediction accuracy. Conclusions: The proposed prediction approach can be used in the prediction of donor splice sites with higher accuracy using short sequence motifs and hence can be used as a complementary method to the existing approaches. Based on the proposed methodology, a web server was also developed for easy prediction of donor splice sites by users and is available at http://cabgrid.res.in:8080/sspred.

Density-effect and economic threshold of purple nutsedge ( Cyperus rotundus ) in soybean

Abstract: Purple nutsedge (~nutsedge) is an important perennial weed, which infests soybean in India and causes high yield losses. Selective pre-emergence herbicides hardly control nutsedge. Post-emergent application of imazethapyr is effective against nutsedge with almost 70 % efficiency. Information on the interference effect of nutsedge across densities on soybean and its economic threshold (ET) is hardly available, but would be useful for its management, and saving herbicide treatments with lower densities. An experiment was designed to evaluate the interference of nutsedge in pure stands, and that of natural weed infestations on soybean. Moreover, it was aimed to determine ET of nutsedge in soybean. The dry weights of weeds in the treatments ‘natural weeds including nutsedge’ and the one of nutsedges in the pure stand density of nutsedge 200 plants/m2 were similar and higher than weed biomass in other nutsedge densities. The ‘natural weed infestation both including and excluding nutsedge’ and the treatment of 200 nutsedge plants/m2 caused greater reductions in soybean yields and were the most competitive. The ET of nutsedge in soybean was 19–22 (~mean 21) plants/m2, considering 70 % efficiency of the herbicide imazethapyr. It predicts that a density of 21 nutsedge plants/m2 can cause 9.1–11.5 % yield losses, which are an economic loss under this situation. This ET would help in making decisions for nutsedge management and fitting models and could be used for other similar sites with nutsedge dominance. This ET, considering several production factors, is more precise and reliable than the ET determined with only yield losses.

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

Locus minimization in breed prediction using artificial neural network approach.

Abstract: Molecular markers, viz. microsatellites and single nucleotide polymorphisms, have revolutionized breed identification through the use of small samples of biological tissue or germplasm, such as blood, carcass samples, embryos, ova and semen, that show no evident phenotype. Classical tools of molecular data analysis for breed identification have limitations, such as the unavailability of referral breed data, causing increased cost of collection each time, compromised computational accuracy and complexity of the methodology used. We report here the successful use of an artificial neural network (ANN) in background to decrease the cost of genotyping by locus minimization. The webserver is freely accessible (http://nabg.iasri.res.in/bisgoat) to the research community. We demonstrate that the machine learning (ANN) approach for breed identification is capable of multifold advantages such as locus minimization, leading to a drastic reduction in cost, and web availability of reference breed data, alleviating the need for repeated genotyping each time one investigates the identity of an unknown breed. To develop this model web implementation based on ANN, we used 51,850 samples of allelic data of microsatellite-marker-based DNA fingerprinting on 25 loci covering 22 registered goat breeds of India for training. Minimizing loci to up to nine loci through the use of a multilayer perceptron model, we achieved 96.63% training accuracy. This server can be an indispensable tool for identification of existing breeds and new synthetic commercial breeds, leading to protection of intellectual property in case of sovereignty and bio-piracy disputes. This server can be widely used as a model for cost reduction by locus minimization for various other flora and fauna in terms of variety, breed and/or line identification, especially in conservation and improvement programs.

Calibration-Based Product Estimator in Single- and Two-Phase Sampling

Abstract: An estimator of population total is developed using the calibration approach under the assumption that the auxiliary variable is negatively correlated with the study variable. The proposed estimator outperforms the usual product estimator in terms of the criteria of bias and mean square error. An improved estimator of the variance of the proposed estimator is developed using the calibration approach a second time. The two-phase sampling case has also been dealt with. The theoretical results are demonstrated through a simulation study.

Structural Breaks in Mean Temperature over Agroclimatic Zones in India

Abstract: Amongst Asian countries India is one of the most vulnerable countries to climate change. During the past century, surface temperature in India has shown a significant increasing trend. In this paper, we have investigated behavior of mean monthly temperature during the period 1901–2001 over four agroclimatic zones of India and also tried to detect structural change in the temperature series. A structural break in the series has been observed at the national as well regional levels between 1970 and 1980. An analysis of trends before and after the structural break shows a significant increase in July temperature in the arid zone since 1972.

Neighbor-Balanced Row-column Designs

Abstract: In this article, row-column designs incorporating directional neighbor effects have been studied. A row-column design is said to be neighbor balanced if every treatment has all other treatments appearing as a neighbor a constant number of times. We considered here three different situations under row-column setup incorporating neighbor effects viz., row-column design with one-sided neighbor effect, two-sided neighbor effect, and four-sided neighbor effect. The information matrices for all the situations for estimating the direct and neighbor effects of treatments have been derived. Methods of constructing neighbor-balanced row-column designs have been developed and its characterization properties have been studied.

Calibration Approach-Based Regression-Type Estimator for Inverse Relationship Between Study and Auxiliary Variable

Abstract: A regression-type estimator of population total is developed using the calibration approach under the assumption that the auxiliary variable is negatively correlated with the study variable. An estimator of the variance of the proposed estimator is developed. In addition, a higher order calibration approach has been described for the estimator of the variance of the proposed estimator. When the auxiliary information is not available on all the population units, then a two-phase sampling approach has been suggested. Theoretical results obtained are demonstrated through simulation studies and also with real data. Empirical results show that the proposed estimator outperforms the usual regression estimator in terms of the criteria of bias and mean square error.

An online software for decision tree classification and visualization using c4.5 algorithm (ODTC)

Abstract: 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.

Efficient Incomplete Block Designs Through Linear Integer Programming

Abstract: SYNOPTIC ABSTRACTThe purpose of this paper is to present a linear integer programming approach to construct efficient binary incomplete block designs for any given number of treatments v, number of blocks b, with common block-size k, and with a nearly balanced concurrence matrix. The proposed approach is illustrated by constructing an efficient incomplete block design. A-efficient and D-efficient incomplete block designs have been constructed and catalogued using the proposed algorithm for a restricted range of parameters 3 ⩽ v ⩽ 20, b ⩾ v, and 2 ⩽ k ⩽ min(10, v − 1), with vb⩽1, 000. An R package is developed to implement the proposed approach.

Thermodynamics of cadmium sorption on different soils of West Bengal, India.

Abstract: A sorption study was conducted on different soils collected from five agroecological zones of West Bengal, India, to understand the soil environmental behavior and fate of cadmium. For this purpose batch adsorption experiments were carried out at the native soil pH and at three different temperatures (25°C, 35°C, and 45°C). The adsorption data fitted by a linear least squares technique to the different sorption isotherms. Most data obtained give the good fit to both Freundlich and modified Langmuir isotherms, but they are not consistent with the linear Langmuir adsorption model. Thermodynamic parameters, namely, thermodynamics equilibrium constant at a particular temperature T (K T (0)), Gibbs free energy at a particular temperature T (ΔG T (0)), and change of enthalpy (ΔH (0)) and change of entropy at temperature T (ΔS T (0)), were also determined by applying sorption value and concentrations of Cd in equilibrium solution within the temperature range. The thermodynamic parameters revealed that Cd sorption increases as the values of K T (0), ΔG T (0), ΔH (0), and ΔS T (0) were increased on reaction temperatures. The spontaneous sorption reaction can be concluded due to high values of ΔG T (0). The positive values of ΔH (0) indicated that the Cd sorption is an endothermic one. Under these present conditions, the soil and its components possibly supply a number of sites having different adsorption energies for cadmium sorption.

Outliers in Multi-Response Experiments

Abstract: Cook-statistic has been developed for detecting outliers in two likely situations of occurrence of outliers in multi-response experiments. In the first situation, more than one outlying observations vector has been considered. Each of these vectors is obtained on the assumption that a particular observation from each of the responses is an outlier. A general expression of Cook-statistic for detecting any such t outlying observations vectors has been obtained. Then some particular cases have been considered. In the second case a situation is considered where observations from all the responses may not be outliers. Here also a general expression of Cook-statistic is obtained for detecting any t observations from each of any k responses as outliers. In both the cases Cook-statistic is applied to real experimental data.

Robustness of Variance Balanced Block Designs

Abstract: The robustness of block designs against missing observations is revisited. It has been shown that A-efficiency criterion is not an appropriate measure to judge the efficiency of the residual design. As an alternate to this, E-efficiency criterion is defined. A lower bound of this criterion for the loss of any t observations in binary variance balanced block design is obtained. Balanced incomplete block designs (BIBD) that are robust as per E-efficiency criterion are identified.

Did Tsunami tremor jolt microbial biomass and their activities in soils? A case study in Andaman Island, India

Abstract: The nature of selected soil-chemical and microbial properties influenced by tsunami affected and non-affected areas along the border areas of the alluvial Andaman Island in India were investigated. Soils of these areas have turned saline and saline-sodic due to the ingression of sea water. The electrical conductivity of the saturation extract of the surface soil varied from 11.2 to 23.8 dS m−1 in 2005, and it was decreased to 0.8–10.3 dS m−1 in 2006 due to the heavy rain in the following year after the tsunami. Soil quality indicators, like microbial biomass C, microbial metabolic quotient, microbial respiration quotient and fluorescein diacetate hydrolyzing activity, decreased in the tsunami affected soil in 2005, but slightly increased in 2006. All microbial parameters were significantly negatively correlated with the electrical conductivity, sodium absorption ratio and exchangeable sodium percentage. Suppression of microbial biomass and their activities in the soils due to the increased-salinity is of great agronomic significance and needs suitable intervention for sustainable crop production. Significant differences were found in soil-chemical and microbial characteristics between tsunami affected and non-affected areas. Hierarchical clustering algorithm on the basis of different soil-chemical and microbial characteristics revealed that there is significant difference in grouping between tsunami affected and non-affected zones. From this study, it can be concluded that the sea water ingression detrimentally influenced the microbial properties of tsunami affected soil.

Classification of Cereal Proteins Related to Abiotic Stress Based on their Physicochemical Properties Using Support Vector Machine

Abstract: Abiotic stress factors severely limit plant growth and development as well as crop yield. There is a great need to develop understanding of plant physiological responses to abiotic stresses in order to improve crop productivity through crop improvement programmes. Proteins play a central role in plant adaptations under stress and hence their identification is important to the biologist. Identification of such proteins by wet lab experimentation is sometimes expensive and timeconsuming. In such a situation, in silico approaches can be used to narrow down this search. In this study, classification of cereal proteins subjected to four different stresses, namely, extreme temperature, drought, salt and abscisic acid (ABA) was undertaken. Classification models were built using support vector machine (SVM) to predict the function of proteins under these abiotic stresses on the basis of 34 physicochemical features extracted from the protein sequence. Specific features of the protein sequence that are highly correlated with certain protein functions were selected by stepwise logistic regression, a feature selection method. SVM was trained using different kernel functions and cross-validated using 10-fold crossvalidation technique. Prediction precision was assessed through different measures such as sensitivity, specificity and accuracy. The accuracy of protein function prediction using SVM with different kernel functions ranges from 60% to 100%.