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Not AvailableAn agriculture-dominated developing country like India has been always in need of efficient and reliable time series forecasting methodologies to describe various agricultural phenomenons, whereas agricultural price forecasting continue to be the challenging areas in this domain. The observed features of many temporal price data set constitute complex nonlinearity, and modeling these features often go beyond the capability of Box–Jenkins autoregressive integrated moving average methodology. Moreover, despite the popularity and sheer power of traditional neural network model, the empirical forecasting performance of this model has not been found satisfactory in all cases. To address the problem, wavelet-based modeling approach is recently upsurging. Present study discusses two wavelet-based neural network approaches envisaging monthly wholesale onion price of three markets, namely Bangalore, Hubli, and Solapur. Wavelet-based decomposition makes it possible to describe the useful pattern of the series from both global as well as local aspects and found to be highly proficient in denoising and capturing the inherent pattern of the series through a distinctive approach. Besides, wavelet method can also be used as a tool for function approximation. The improvement upon time-delay neural network also be made up to a great extent through using wavelet-based approaches as exhibited through proper empirical evidence.Not Availabl

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Not AvailableThe Sustainable Development Goal of Zero Hunger is a bold commitment towards 795 million undernourished people to end all forms of hunger and malnutrition by 2030 (http://www.undp.org/sustainable-development-goals/goal-2-zero-hunger/). India, sharing a quarter of the global hunger burden, has set a comprehensive action against the food insecurity and hunger issue through microscopic identification of food insecure mass followed by decentralized level planning and effective monitoring. Availability of reliable disaggregate level statistics using Small Area Estimation (SAE) approach for measuring the prevalence of food insecurity can be a potential key to the Governmental organization to take consistent steps towards framing strategic plans eyeing zero hunger. A pragmatic approach in SAE is to consider Hierarchical Bayes (HB) framework, which provide an added flexibility of using complex models without concerning much about known design variance or traditional normality assumption. However, this approach does not incorporate the survey weights that are essential for valid inference given the informative samples that are produced by complex survey designs. In this paper, involving survey design information a number of model specifications are discussed in area level HB version to generate reliable and representative district and district by social groupwise estimates of food insecurity incidence for rural areas of the State of Odisha in India by combining the Household Consumer Expenditure Survey 2011-2012 data of National Sample Survey Office and with the Population census 2011. Spatial maps have been produced to observe the inequality in food insecurity distribution among the districts as well as districts cross classified by socio-economic categories. Such maps are definitely useful for policy formulation, fund disbursement purpose and for the Government in taking effective administrative decisions targeting zero hunger.Not Availabl

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Not AvailableSustainable development goal-1 of the United Nations is to end poverty in all its forms everywhere. The estimates of poverty related parameters obtained from large scale sample survey are often available at large domain level (e.g. state level). But, poverty rates are not uniformly distributed across the regions. The regional variations are masked in such large domain level estimates. However, for monitoring the progress of poverty alleviation programmes aimed at reduction of poverty often require micro or disaggregate level estimates. The traditional survey estimation approaches are not suitable for generating the reliable estimates at this level because of sample size problem. It is the main endeavor of Small Area Estimation (SAE) approach to produce micro level statistics with acceptable precision without incurring any extra cost and utilizing existing survey data. In this study, the Hierarchical Bayes approach of SAE has been applied to generate reliable and representative district level poverty incidence for the State of Odisha in India using the Household Consumer Expenditure Survey 2011–2012 data of National Sample Survey Office and linked with Population Census 2011. The results show the precise performance of model based estimates generated by SAE method to a greater extent than the direct survey estimates. A poverty map has also been produced to observe the spatial inequality in poverty distribution.Not Availabl

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Not AvailableInformation about the household debt behaviour in different occupational categories is of key importance to the Governmental organization for taking effective policy measures targeting the vulnerable groups. This paper illustrates small area estimation (SAE) methodology to estimate proportion of indebted households in rural areas for the two major occupation categories- rural cultivator and rural non-cultivator as well as for both categories combined together across all the 30 districts of Karnataka state in India using the data of All India Debt and Investment Survey 2012-13 and population census 2011. The findings show that the district-level estimates of incidence of indebtedness obtained from SAE are more precise than the direct survey estimates. A spatial map has also been produced to observe the inequality in distribution of indebtedness within districts and in each occupational category across districts. Such maps are definitely useful for framing consistent policy actions and fund disbursement for the indebted household mass.Not Availabl

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Not AvailableAgriculture is the key livelihood for the vast majority of population in India. The sector is such a crucial that prosperity of agrarian community is essential for Government/Institutional stability. Therefore, the accurate estimation of production in terms of harvested area and yield are equally important in ensuring the accurate determination of their product. Although the yield estimation gets most of the attention, there are many complexities to the estimation of area that might not be readily apparent. Crop area statistics in most of the states are furnished based on complete enumeration or census method. But, shortage of man power, failure of the primary and revenue staffs to devote adequate time and attention in collection and compilation of data has deteriorated the quality of area statistics as well as increased the time lag in availability of data in hand. In the view of above problem, a well-designed sample survey has the ability to cater the need of accurate crop area information and is especially important in developing countries which have very limited resources to apply to the collection of agricultural data. A pilot experiment conducted by ICAR- Indian Agricultural Statistics Research Institute, New Delhi attempts to estimate district level crop yield based on reduced number of Crop Cutting Experiments (CCEs) while crop acreage estimation has been done through well designed sample survey approach. But, traditional sampling theory has also some limitations in providing reliable and valid estimates particularly for districts with few or negligible sample sizes. To tackle the need of representative crop acreage estimation at disaggregated level, Small Area Estimation (SAE) approach has been considered in this paper. In particular, using Hierarchical Bayes spatial small area model disaggregated level crop area has been estimated for two major crops, rice and wheat respectively in the state of Uttar Pradesh for Agriculture year 2015-16. Estimates produced using SAE technique has acceptable precision level and is a positive attempt of crop acreage estimation at micro or local level through SAE approach in India.Not Availabl

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Not AvailableSeveral factors, including weather vagaries, possess a serious threat to agricultural crop production in India and also are noteworthy risks to the economy. Crop yield depends on nutrition level of soils, fertilizer availability and cost, pest control, agro-meteorological input parameters like temperature, rainfall and other factors. Further, each particular crop needs specific growing weather conditions. Therefore, prognosticating crop yield is a challenging task for every nation. Statistical models are the most commonly used tools to forecast the crop yield, whereas statistical forecasting model for predicting dynamic behavior of crop yield should be able to take advantage not only of historical data of crop yield, but also the impact of various driving forces of the external environment. This paper describes both the linear regression and time-series models to predict crop yield efficiently and precisely. In particular, Bajra yield data for Alwar district of Rajasthan have been considered for empirical fitting of the models. Additionally, the selection of auxiliary variables, based on the knowledge of crop growth stages, has mediated the outperformance of time-series model.Not Availabl

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Not AvailableSeveral factors, including weather vagaries, possess a serious threat to agricultural crop production in India and also are noteworthy risks to the economy. Crop yield depends on nutrition level of soils, fertilizer availability and cost, pest control, agro-meteorological input parameters like temperature, rainfall and other factors. Further, each particular crop needs specific growing weather conditions. Therefore, prognosticating crop yield is a challenging task for every nation. Statistical models are the most commonly used tools to forecast the crop yield, whereas statistical forecasting model for predicting dynamic behavior of crop yield should be able to take advantage not only of historical data of crop yield, but also the impact of various driving forces of the external environment. This paper describes both the linear regression and time-series models to predict crop yield efficiently and precisely. In particular, Bajra yield data for Alwar district of Rajasthan have been considered for empirical fitting of the models. Additionally, the selection of auxiliary variables, based on the knowledge of crop growth stages, has mediated the outperformance of time-series model.Not Availabl

Quantitative detection of pathogen load of Fusarium oxysporum f.sp. ciceris infected wilt resistant and susceptible genotypes of chickpea using intergenic spacer region-based marker

Highlights • Intergenic spacer based markers are robust molecular markers for detection of phytopathogenic fungi. • qPCR can be utilized as a molecular diagnostic tool to quantify pathogen DNA load at pictogram (pg) level. • Differential dynamics of pathogen DNA in chickpea genotypes contrasting for Fusarium wilt resistance was observed.Quantitative detection of pathogen DNA load is a crucial aspect in development of disease management strategies and breeding programs. In recent years, there have been several reports where formae speciales specific intergenic spacer (IGS) sequence based markers have been used for quantification of pathogen DNA in different plant and soil samples, through quantitative real-time PCR (qPCR). In the present study, we have utilized an IGS based marker, ISR 52, to detect and quantify Fusarium oxysporum f.sp. ciceris (Foc) DNA, using both conventional PCR and qPCR, in chickpea genotypes which contrast for resistance to Fusarium wilt. Our study reveals that the Foc DNA load was found to be significantly higher in the early wilting genotypes as compared to the wilt resistant genotypes. Late wilting genotype showed a spike in pathogen DNA load in later stage of plant growth. Phenotypic observation of disease progression in combination with qPCR data validated that the pathogen undergoes incubation period before manifestation of symptoms. The above observations provide evidence about the differential dynamics of pathogen build up inside different hosts during different time periods and probable reason for the earliness, lateness and resistance in wilting like traits in these genotypes