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Not AvailableHorticulture sector, a significant sector of agriculture, has emerged as an important component of the Indian economy contributing more than one-fourth share in the economy of agriculture and allied sectors. Fruits and vegetables account for nearly 90% of the total horticulture production in the country. Availability of reliable statistics about area and production of these crops at various levels has been one of the basic requirements of proper planning for increasing the production of these crops in the country. The estimates of area and production of important fruits and vegetables were obtained under the scheme “Crop Estimation Survey on Fruits and Vegetables (CES - F&V)” only for eleven states. An alternative sampling methodology for estimation of area and production of different horticultural crops was developed by Ahmad et al. (2011). In this paper, an attempt was made to estimate the area, production and productivity of important fruits and vegetables for Himachal Pradesh State using the alternative sampling methodology. Optimum sample size was also determined for obtaining reliable estimates of production of fruits and vegetablesNot Availabl

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Not AvailableRanked Set Sampling (RSS) is preferred over Simple Random Sampling (SRS) when measuring an observation is expensive or time consuming, but can be easily ranked at a negligible cost. Biswas et al. (2015) proposed a Spatial Estimator (SE) of population mean under RSS through prediction approach incorporating spatial dependency among sampling units of a spatial finite population. In this present article, an attempt has been made to propose bootstrap techniques viz. Rescaled Spatial Stratified Bootstrap (RSSB) and Rescaled Spatial Clustered Bootstrap (RSCB) methods for unbiased variance estimation of the SE under RSS from finite populations. Simulation study reveals that both the proposed methods give approximately unbiased estimation of variance of the SE under RSS for different combination of sample and bootstrap sample sizes, but while considering relative stability, RSSB method was found to be more stable.Not Availabl

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Not AvailableIn this study, an attempt has been made to improve the sampling strategy incorporating spatial dependency at estimation stage considering usual aerial sampling scheme, such as simple random sampling, when the underlying population is finite and spatial in nature. Using the distances between spatial units, an improved method of estimation, viz. spatial estimation procedure, has been proposed for the estimation of finite population mean. Further, rescaled spatial bootstrap (RSB) methods have been proposed for approximately unbiased estimation of variance of the proposed spatial estimator (SE). The properties of the proposed SE and its corresponding RSB methods were studied empirically through simulationNot Availabl

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agricultural development, computer software, economic indicators, indexes, sensitivity analysisSensitivity analysis is the study of how the given composite index depends upon the information fed into it. In this paper, methodological issues for sensitivity analysis of various indicators of composite index have been reviewed and sensitivity analysis using empirical method of variance-based technique has been proposed. The proposed method has been used for sensitivity analysis of indicators and sub indices of Agricultural Development Index (ADI) constructed for all 38 districts of Bihar State. Sensitivity analysis has also been carried out using a software namely SIMLAB, that is especially designed for sensitivity analysis. The results of sensitivity analysis using empirical method of variance-based technique have been compared with the results obtained using SIMLAB software. It has been observed that ADI was highly sensitive to Infrastructure index followed by Output index and Input index as per analysis using both the approachesNot Availabl

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Not AvailableRanked Set Sampling (RSS) is preferred over Simple Random Sampling (SRS), when measuring an observation is expensive or time consuming, but can be easily ranked at a negligible cost. While working with spatial population, classical statistical methods fail to capture the dependency present in the underlying data. In this article, an attempt was made to develop efficient estimation procedure through RSS sampling design incorporating spatial dependency among sampling units of a spatial finite population. Distance between spatial units was taken as measure of spatial dependency. The properties of the proposed Spatial Estimator (SE) were further studied empirically through a simulation study. The proposed Spatial Estimator (SE) under RSS of population mean from spatial data was found to be better than usual RSS estimatorNot Availabl

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Not AvailableHorticultural crop plays a unique role in India’s economy, therefore reliable and timely estimates of area under horticulture crops are of vital importance. Present methods of crop acreage estimation rely heavily on sample survey approach which is time consuming for a diversified and large country like India. Modern space technology with advance tools of Remote Sensing, GIS and GPS may be an alternative option for estimating area under horticultural crops. The advantage of using satellite data is that it provides both synoptic view and the economies of scale, since data over large areas could be gathered quickly from such platforms. This study has been undertaken to estimate the acreage under mango and to map existing orchards of Mango using hyperspectral satellite data. The study was conducted for Meerut district of Uttar Pradesh. The hyperion hyperspectral satellite data was evaluated to estimate the area under all mango orchards. These estimates were compared with actual area under mango orchards measured using Global Positioning System (GPS) and the total area under mango was predicted as 961.88 ha which was 92% close to ground data 889.65 ha. The results indicated the scope of hyperspectral remote sensing in acreage estimation of fruit crops.Not Availabl

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Not AvailableRemote sensing technology is the powerful tool for mapping because of its wide area coverage, gives information about inaccessible area, timely repetitive coverage of the same area. However, the image analysis is a challenging task. Digital classification is not always efficient, particularly if there are extreme variations in land cover as it exists in north eastern part of the country. In this study, classical technique of visual interpretation along with image enhancement technique was adopted for classification. In addition to this, classification through unsupervised and supervised techniques has also been followed to compare the relative accuracies. The result shows that classical technique of visual interpretation is a better way to classify the land use/land cover particularly for the hilly region where the algorithm based classification fails due to the very undulating topographical condition and complex spectral signature of vegetation.Not Availabl

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Not AvailableThe 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.Not Availabl

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Not AvailableWhen survey data shows spatial non-stationarity then geographically weighted regression (GWR) approach explains the data more effectively than standard global regression model. In this article, two outlier robust geographically weighted regression (RGWR) estimators have been proposed to estimate the finite population total under spatial nonstationarity. The first RGWR estimator is based on winsorization whereas second one is based on filtering of outliers. In order to compare the statistical performance of proposed estimators with standard non-robust GWR estimator and a robust estimator proposed by Chamber (1986), a simulation study was carried out. It has been observed that proposed estimator based on winsorization of sampled data performs fairly well in a scenario where spatial non-stationarity appears in population and the survey data contains outliersNot Availabl

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Not AvailableThis study was conducted to understand the behaviour of ten rice genotypes for different water deficit stress levels. The spectroscopic hyperspectral reflectance data in the range of 350–2500 nm was recorded and relative water content (RWC) of plants was measured at different stress levels. The optimal wavebands were identified through spectral indices, multivariate techniques and neural network technique, and prediction models were developed. The new water sensitive spectral indices were developed and existing water band spectral indices were also evaluated with respect to RWC. These indices based models were efficient in predicting RWC with R2 values ranging from 0.73 to 0.94. The contour plotting using the ratio spectral indices (RSI) and normalized difference spectral indices (NDSI) was done in all possible combinations within 350–2500 nm and their correlations with RWC were quantified to identify the best index. Spectral reflectance data was also used to develop partial least squares regression (PLSR) followed by multiple linear regression (MLR) and Artificial Neural Networks (ANN), support vector machine regression (SVR) and random forest (RF) models to calculate plant RWC. Among these multivariate models, PLSR-MLR was found to be the best model for prediction of RWC with R2 as 0.98 and 0.97 for calibration and validation respectively and Root mean square error of prediction (RMSEP) as 5.06. The results indicate that PLSR is a robust technique for identification of water deficit stress in the crop. Although the PLSR is robust technique, if PLSR extracted optimum wavebands are fed into MLR, the results are found to be improved significantly. The ANN model was developed with all spectral reflectance bands. The 43 developed model didn’t produce satisfactory results. Therefore, the model was developed 44 with PLSR selected optimum wavebands as independent x variables and PLSR-ANN model 45 was found better than the ANN model alone. The study successfully conducts a comparative 46 analysis among various modelling approaches to quantify water deficit stress. The methodology developed would help to identify water deficit stress more accurately by predicting RWC in the crops.Not Availabl