Issues relating to poverty have been at the core of development process in all developing countries. Measurement of poverty has been extremely important for evaluation of development strategies. Disparities do exist in the income as well as consumption and expenditure levels in different groups of society as also there are spatial dispersions. There are indicators for measuring incidence, depth and severity of poverty. Most of these indicators are estimated at State level with the help of data as obtained from Consumption Expenditure Surveys. For poverty alleviation programmes, as well as for planning other development strategies at micro-level, small area level estimates for poverty indicators are necessary. In this paper, an attempt is made to review some of the existing procedures for poverty mapping and an application of a Small Area Estimation technique is made for estimating poverty indicators at district level in Uttar Pradesh. Data from Consumption Expenditure Survey of NSSO (61st round 2004-05) has been used for this application. Key words: Poverty indicators, Small Area Estimation, Area level models.

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  The level of development of different districts of Andhra Pradesh was obtained with the help of composite index based on optimum combination of fifty socio-economic indicators. The district-wise data for the year 2001-02 in respect of these fifty indicators were utilized for 22 districts of the State. The level of development was estimated separately for agricultural sector, infrastructural facilities and overall socio-economic sector. The district of West Godavari was ranked first in overall socio-economic development and the district of Guntur was found on the first position in respect of agricultural development. Wide disparities were observed in the level of development among different districts. Infrastructural facilities were found to be positively associated with the level of developments in agricultural sector and overall socio-economic field. Agricultural development was influencing the overall socio-economic development in the positive direction. Key words: Developmental indicators, Composite index, Potential targets, Model districts.

  Employing the customary predictive format, as alluded to by Basu (1971), Smith (1976) and several others, for estimation of the population total or the population mean under a fixed population set-up, we have generated a sequence of efficient unbiased poststratification-based estimators. The proposed sequence of estimators is found, under optimal and non-optimal conditions, to be more efficient than the customary poststratified estimator and the usual simple mean. The performance of the proposed sequence of estimators has been examined from the point of view of conditional randomization inference. Key words: Poststratification, Conditional randomization inference, Non-optimal better estimators.

  Ranked set sampling is applicable whenever ranking of a set of sampling units can be done easily by a judgement method or based on the measurement of an auxiliary variable on the units selected. In this work, we derive different estimators of the parameters associated with the distribution of the study variate Y, based on ranked set sample obtained by using an auxiliary variable X correlated with Y for ranking the sample units, when (X, Y) follows a Morgenstern type bivariate logistic distribution. The theory developed in this paper is illustarted using a real data. Efficiency comparison among these estimators are also made. Key words: Ranked set sample, Morgenstern type bivariate logistic distribution, Best linear unbiased estimator, Concomitants of order statistics.

  The population of the endangered coldwater fish species, Tor putitora has been sharply declined in the recent past and is threatened with multifaceted dangers. In the present investigation, an attempt has been made to develop the length-weight relationship of Tor putitora under monoculture and polyculture systems for direct use in fishery assessment and also to describe growth pattern in terms of weight of this fish species in the above two different culture systems. The von-Bertalanffy model was found to be the best suitable model to describe the growth pattern of Tor putitora. Key words: Endangered, Fish stock, Growth pattern, Monoculture, Polyculture.

  Nutrient Management plays a vital role in increasing crop production, soil upgradation and increase in profitability. Taking these things into consideration, a Decision Support System on Nutrient Management in Crops (DSSNMC) has been designed and developed at Indian Agricultural Statistics Research Institute (IASRI). DSSNMC is a Web-based Decision Support System (DSS) and provides decision to farmers on nutrient management in crops. The system will have great importance in agriculture as experts are not always available to answer farmers? queries. DSSNMC has three modules to provide decision support to farmers in three different situations. First module is the subsystem based on soil test values. Herein, the user gets an advice for fertilizer application based on the information provided for soil test values, crop to be grown, variety of that crop, sowing season, soil type and targeted yield (within a particular range). In case, soil was not tested, then the farmer can use the second module which provides decision support on the basis of location such as zone or district. The system requires information of the location of the farm in terms of zone or district, targeted yield and rest of the values like available nitrogen, phosphorus, potassium and the soil pH are taken from the data base, where standard values for different districts or zones are stored. Third module of the system helps in controlling nutrient deficiency of standing crops based on abnormal growth as seen through deficiency symptoms shown by the crop. The basis here is the observation of the farmers, which they compare with the images, stored in the system and can use the corrective measures provided by the system. The testing and validation of the system was done using the data of different cooperating centers under All India Coordinated Research Project (AICRP) of Soil Crop Response Correlation (STCR). Key words: Decision support system, Nutrient management, Web application, .Net technologies.

  With the advent of computers, the development of accurate forewarning systems for incidence of crop diseases has been increasingly emphasized. Timely forewarning of crop diseases will not only reduce yield losses but also alert the stakeholders to take effective preventive measures. Traditionally, Logistic Regression (LR) and discriminant analysis methods have been used in forewarning systems. Recently, several machine learning techniques such as decision tree (DT) induction, Rough Sets (RS), soft computing techniques, neural networks, genetic algorithms etc. are gaining popularity for predictive modelling. This paper presents the potential of three machine learning techniques viz. DT induction using C4.5, RS and hybridized rough set based decision tree induction (RDT) in comparison to standard LR method. RS offers mathematical tools to discover hidden patterns in data and therefore its application in forewarning models needs to be investigated. A DT is a classification scheme which generates a tree and a set of rules representing the model of different classes from a given dataset. A java implementation of C4.5 (CJP) is used for DT induction. A variant of RDT called RJP, combines merits of both RS and DT induction algorithms. Powdery mildew of Mango (PWM) is a devastating disease and has assumed a serious threat to mango production in India resulting in yield losses of 22.3% to 90.4%. As a case study, prediction models for forewarning PWM disease using variables viz. temperature and humidity have been developed. The results obtained from machine learning techniques viz. RS, CJP and RJP are compared with the prediction model developed using LR technique. The techniques RJP and CJP have shown better performance over LR approach. Key words: Forewarning crop diseases, Machine learning, Rough sets, RDT, Decision tree, Logistic regression, Powdery mildew of mango.

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