Horticultural crops unlike field crops are perennial in nature, not having distinct phenology. It is difficult to discriminate horticultural crops using temporal multispectral data. Major limitation of multispectral data is lesser number of bands and mixed pixels which may not be able to discriminate fruit crops but the hyperspectral data has the advantage of having relatively large number of narrow, contiguous bands which lead to continuous spectral reflectance curve, making intricate details visible in the spectrum. For comparison of multispectral data with hyperspectral data, the hyperspectral data which have 2151 numbers of bands has been brought to multispectral level as because multispectral data has very less number of bands. Therefore, in the hyperspectral data, average at 50 nm, 100 nm and 250 nm interval was taken to reduces the data set into 42, 22 and 9 bands. of bands. Therefore, in the hyperspectral data, average at 50 nm, 100 nm and 250 nm interval was taken to reduces the data set into 42, 22 and 9 bands. of bands. Therefore, in the hyperspectral data, average at 50 nm, 100 nm and 250 nm interval was taken to reduces the data set into 42, 22 and 9 bands. The 4 tier statistical procedure which includes one way Analysis of variance (ANOVA), Classification and regression tree (CART), Jeffries-Matusita (J-M) distance and Linear discriminant analysis (LDA) technique was applied in the reduced band data set. The result of J-M distance and LDA were (J-M) distance and Linear discriminant analysis (LDA) technique was applied in the reduced band data set. The result of J-M distance and LDA were used to observe whether the reduced band data set can be able to discriminate the fruit crops. The study reveals the limitation of multispectral data in fruit crop discrimination. As the number of bands gets reduced the discriminative power of the data set also gets down. Keywords:Classification and regression tree, Discrimination, Hyperspectral data, Jaffries-Matusita distance, Linear discriminant analysis, Multispectral data, One way analysis of variance.

  data, One way analysis of variance. data analysis, sampling weights must be used to incorporate the sample designs. Regression coefficients are estimated to find the relationship between the study and auxiliary variables. Kish and Frankel (1974) deliberated the use of sampling weights in the estimation of regression coefficients. This paper describes calibration based approach to estimate the regression coefficient using two auxiliary variables. The variance estimation of proposed paper describes calibration based approach to estimate the regression coefficient using two auxiliary variables. The variance estimation of proposed estimator is also developed. The empirical results based on synthetic and real population show that the proposed estimator, in terms of percent relative bias and percent relative root mean square error, performs better than the existing estimator. The proposed variance estimator shows a satisfactory performance in empirical evaluation. Keywords: Calibrated weights, Regression coefficients, Auxiliary variable.

  The apparent use of total food grains produced in any country is for the purpose of human consumption, animal feed and seed requirements. Despite all possible preventive efforts, large quantities are also wasted from the time the grain is harvested till it reaches the consumer. The purpose of this paper is to discuss the outcome of a pilot study for estimation of seed, feed and wastage ratio of major food grains for the state of Odisha of India. The data collection work in Odisha state was conducted in the Agricultural Year, 2014-15. In this study our aim was to reevaluate the current seed, feed and wastage ratio of major food grain crops based on improved sampling design and reduced sample sizes proposed under this pilot project. Through this study it was found that the seed, feed and wastage ratio of major food grain crops was reduced to around 7 % from the present 12.5 % in India. Keywords: Seed feed and wastage ratio, Crop estimation surveys, Two stage sampling, Major food grain crops.

  The Vasai creek, the Manori creek and the Thane creek are estuarine creeks in the Arabian Sea near Mumbai. This area has the highest economic development rates in India. In this estuarine area, extensive land use change including embankments was observed and various constructions have taken place due to rapid urbanization and industrialization. Improper and unplanned sustainable coastal zone management may lead to severe environmental problems such as sea water intrusion, coastal erosion, siltation of river channels and land subsidence, etc. This study evaluates the utility of satellite remote sensing imageries by deploying multi- temporal Landsat series satellite data like Multispectral scanner (MSS), Thematic mapper-5 (TM5) and Operational land imager (OLI) and high-resolution Google earth imagery including a topographic map of Mumbai also. From the change analysis performed through this study, huge variations in the position of the coastline were observed. The Thane creek shows very drastic change near Sewri while Vasai creek near Rai village. The Manori creek shows an overall shrink in its area. At some places on the coastline, large sediment depositions were observed. The Jawaharlal Nehru (JLN) port trust area shows vast change due to the encroachment of sea water. In 1954, the area where current JLN port trust is established has only 0.65 km2 area, but after land reclamation and development in sea water for JLN port trust, the area converted to 3.94 km2 in the year 2015, depicting a vast change of area as 0.5 km2 per year. One of the most noticeable impacts of coastline changes in the study area is the narrowing down of all estuarine creeks at many places and extension of JLN port trust into sea water. Coastline and coastal area change detection are important for environment planners and to protect coasts from climate change. Keywords: Coastal zone, Coastline change detection, Satellite remote sensing, Spatiotemporal change.

  Sterility Mosaic Disease (SMD) is a major biotic stress limiting achievable yield levels of pigeonpea. Studies on field incidence of SMD carried out for four consecutive kharif seasons (2012-15) indicated the commencement of its infestation during second week of August with peak incidence during third week of October to November. Mean incidence of SMD was higher in 2013 (4.5%), 2014 (4.3%), 2012 (3.8%) with the least in 2015 (0.6%). Correlation analyses of SMD incidence with weather parameters of current, one and two weeks prior indicated significant and negative nfluence of evening humidity at current week; significant and positive influence of sunshine of current to two lagged weeks on mean SMD. Whereas, for maximum SMD, significant negative correlation is found with minimum temperature and evening humidity at current, one week and two week lags; significant positive correlation with sunshine at current to two week lags. Besides multiple regression model, advanced statistical models namely autoregressive integrated moving average model with exogenous variable (ARIMAX), support vector regression (SVR) model and artificial neural network (ANN) have been applied for predicting the mean and maximum SMD. A comparative performance of different models carried out in terms of root mean square error (RMSE) and mean square error (MSE) indicated that both MSE and RMSE of SVR model was less in comparison to regression, ARIMAX and ANN models for forecasting the incidence of sterility mosaic disease of pigeonpea. Keywords: Weather, ARIMAX, SVR, ANN, Pigeonpea, Sterility mosaic disease.

  Keywords: Weather, ARIMAX, SVR, ANN, Pigeonpea, Sterility mosaic disease. are quite serious. GHI is highly biased and lacks statistical vigor. It is based on the use of proxy indicators, comprising undernourishment in general population and undernutrition (stunting and wasting) and under-5 mortality in children. Use of these indicators is questionable since their determinants are not limited to hunger. As an alternative, Nigam (2018) suggested use of behavioral responses-based indicators on access to food. In the present note, his arguments on poor performing GHI are further strengthened. Keywords: Global hunger index, Proxy indicators, Stunting, Behavioral responses, Resampling inference, Simulation.

  Global Hunger Index (GHI) is the currently used measure of hunger in different countries. GHI suffers from many limitations, some of which are quite serious. GHI is highly biased and lacks statistical vigor. It is based on the use of proxy indicators, comprising undernourishment in general population and undernutrition (stunting and wasting) and under-5 mortality in children. Use of these indicators is questionable since their determinants are not limited to hunger. As an alternative, Nigam (2018) suggested use of behavioral responses-based indicators on access to food. In the present note, his arguments on poor performing GHI are further strengthened. Keywords: Global hunger index, Proxy indicators, Stunting, Behavioral responses, Resampling inference, Simulation.

  Orthogonal arrays (OAs) and balanced arrays (BAs) in two and three symbols of strength (2m + 1) have been constructed by considering a tactical configuration (? -? - k - v) converted into design parameters by standard relationship. In view of this, two example with OA in two symbols and one example of BA in three symbols of strength five have been added. In the last, example of intercropping experiment with two main crops and eight intercrops has been provided. Keywords: Tactical configuration, Orthogonal arrays (OA), Balanced incomplete block (BIB) design, Doubly balanced incomplete block (DBIB) design, Strength.

  The univariate Box-Jenkins models ended up being extremely helpful in expansive range of time series analysis. Since these models are univariate, they are appropriate just in single series of data and can?t manage the factors which are systematically dependent over space. Be that as it may, the greater part of the climatic marvels is subject to dependent of their neighbourhoods. To address these issues, one ought to consider the model which incorporates systematic dependencies in both space and time. On other hand spatio-temporal modelling fuses the spatial correlation between the observations at neighbouring regions over a timeframe. The autoregressive and moving average components of univariate time series slacked in both space and time is alluded as space time autoregressive moving average (STARMA) model. The spatial information on different location is incorporated by considering spatial weight grid. In this article, an attempt has been made to incorporate the second order uniform spatial weight matrix to model and forecast the spatio-temporal time-series data. Efforts also have been made to include the spatial heterogeneity among locations by considering inverse distance weightage derived from Euclidean distance of Riemannian great circle distance using longitude and latitude of the respective locations. The proposed methodology has been implemented in simulated data.

  Plant protection is one of the major components of crop management process. Crops are under threat of insects and diseases attack from the day they are seeded to the day they are harvested causing a substantial damage to the crop yield that affects adversely to the farmer?s economy. Many factors influence disease development and its intensity in plants, including hybrid/variety genetics, plant growth stage at the time of infection, weather (e.g., temperature, rain, wind, hail, etc.). Wheat (Triticumaestivum L. emend Fiori &Paol.) is an important crop affected by various diseases at different stages. Diagnosing the disease and its management is very important to save the crop from major damages. An Expert System on Wheat Crop Management (Exowhem) has been developed by the Division of Computer Application, ICAR-IASRI, New Delhi in collaboration with ICAR- Crop Management (Exowhem) has been developed by the Division of Computer Application, ICAR-IASRI, New Delhi in collaboration with ICAR 14 important diseases affecting wheat crop stored in its knowledge base. The system works as an information bank for wheat growing farmers that can help them in better crop management in order to enhance productivity and production of wheat in India. The system is available at the URL http://can help them in better crop management in order to enhance productivity and production of wheat in India. The system is available at the URL http://can help them in better crop management in order to enhance productivity and production of wheat in India. The system is available at the URL http://www.iasri.res.in/wheat. Keywords: Wheat, Disease diagnostic system, Expert system, Artificial intelligence.

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