Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a facility organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Small area estimation. The organization has 454 authors who have published 870 publications receiving 7987 citations.

Small area estimation of crop production using spatial models

Abstract: Agricultural surveys for estimation of crop productions in India are designed to obtain reliable estimates at high geographical levels (districts). Due to shift in emphasis of planning from a macro to micro level, there is a strong need to estimate the agricultural production at small area levels such as tehsils (middle administrative unit), blocks (low administrative unit), and even villages. The main objective of this study is to show the potential of spatial models based on geo-statistical techniques of variogram and kriging for estimation of crop production at small area level (low administrative level) through generation of production surface. To achieve this objective, Indian Remote Sensing IRS-1D satellite data for 1997-1998 for Rohtak district of Haryana state have been used. The results of the study indicate that application of spatial models for estimation of production of major crops at small administrative area level has great potential. The predicted yield/production of wheat in the district/tehsil turns out to be comparable to the estimated production of wheat crop through large-scale survey data. The proposed technique can be further explored and refined to provide reliable estimates of crop production at small area levels. Traditionally, in India, agricultural statistics related to crop production is based on two parameters, viz., (i) area under a crop and (ii) yield of a crop. In general, the former is obtained through a complete enumeration using land records whereas the latter is estimated through sample surveys. Because of a phenomenal growth in computer science, sophisticated computer intensive tools are now available, including geographic information system (GIS), which is useful for efficient estimation of spatial parameters. The agricultural parameters related to crop production are geographical (spatial) in nature; therefore, GIS, in combination with spatial statistical techniques, might be a powerful tool for estimation of these parameters. Spatial statistics deals with realization of random variables arranged over a two- dimensional surface (23). Crop production parameters, such as productivity of crop, soil parameters and availability of ground water are geographical in nature, that is, changes in properties of these indicator variables are gradual and directional over space. Therefore, two adjacent fields are likely to be homogeneous in respect to crop production as compared to fields that are far away. It is expected that this spatial relationship may be used to improve estimation of production parameters. Several attempts have been made to develop suitable spatial models to describe effectively spatial relationship. Brun and Stein (3) demonstrated that variables computed from digital elevation models served as independent variables in the regression analysis to predict soil properties. Lark (11) proposed variogram models to specify correlations of errors

Developing computational intelligence method for competence assessment through expert system: An institutional development approach

Abstract: An expert system is computer software that solves real world problems using human knowledge and reasoning skills Knowledge base is the heart and mind of such system where knowledge is stored in the form of rules, databases, heuristics and facts The key conceptual challenge in this approach is the exploitation of the domain knowledge that is vast and mainly in the minds of experts and is a major bottleneck in development The field of Knowledge engineering addresses these challenges by applying AI principles to add new knowledge in the system and extract information with explanation with the powerful mechanism The developed system is an example of such system that allows a user to add decision making rules and provide platform to add new facts from the existing knowledge, by applying heuristics techniques and methods associated with it The evaluation of human resources is the continuous process in any organization It involves a complex and complicated process to match the right competencies for the right job as per the requirements with in the organization It requires the years of experience for the competence assessment and performance manager to build up competent team and utilize the talent of the team to get the job performed in an efficient manner The development of expert system helps in acquiring the knowledge of the domain expert by the process of knowledge engineering to represent it in the form of rules and facts Consequently it helps us to develop the inference process by applying the Heuristics obtained by various statistical techniques such as regression analysis, classification analysis This inference process becomes instrumental to constitute the production rules that help in decision making for the various stake holders in order to perform their work in a strategic manner To achieve this purpose, the developed Inference engine works both forward (evaluation) and backward chaining (sensitivity analysis) In the paper we have presented the design and development of a system that helps the managers in educational institutions to screen, recruit, manage and develop the team of competent faculty members for the enhancement of teaching and learning process The knowledge elicitation technique used for developing the knowledge base of the competence and performance assessment system is the statistical survey method The results of analysis helped us to develop the inference engine and decision making rules The developed system helps to identify a right person for the right job, to add new designations as per the organization's mission and vision planned by the management and makes a mechanized system for its growth The system is designed for the Indian higher technical education system

Modelling annual maximum temperature of India: a distributional approach

Abstract: A random variable can take very large or very small values known as extreme values. In some instances, the researcher’s interest lies mainly on these extreme values like maximum (or minimum) temperature, maximum (or minimum) amount of precipitations, maximum level of flood water, maximum (or minimum) wind speed, maximum (or minimum) level of disease, or pest infestation in a particular crop or season. These types of extreme values can be modelled by generalized extreme value (GEV) distribution, theorized by McFadden in 1978. The type I GEV distribution, i.e. Gumbel distribution, has been extensively studied by many authors, but due to its constant skewness and kurtosis, it has limited practical application. To this end, different generalizations of Gumbel distribution have been proposed in different pieces of literature. In this manuscript, generalized Gumbel (GG) distribution is obtained from generalized multivariate Gumbel (GMVGB) distribution specified in Demirhan and Hamurkaroglu (Journal of Statistical Planning and Inference 141(Adeyemi and Ojo 2003):1141-1152, 2011) and different properties of this distribution have been derived. The continuous increasing of atmospheric concentration of greenhouse gases, mainly due to human activity, has led to global climate change. Climate change can in turn modify the occurrence and intensity of extreme weather and climate events—heat waves and sea-level rise. In the present study, one such climatic variable, i.e. the annual maximum temperature data of India, has been taken for the period of 1901 to 2017 and various extreme value distributions, namely GEV, Gumbel and GG, have been fitted. The obtained results indicate that the GG distribution is better fitted than the other two competing distributions.

Some series of efficiency balanced designs1

Abstract: Summary In this note, some series of efficiency balanced block designs have been obtained.

GinMicrosatDb: a genome-wide microsatellite markers database for sesame (Sesamum indicum L.).

Abstract: Molecular breeding in sesame is still at infancy due to limited number of microsatellite markers available and the low level of polymorphism exhibited by them. Therefore, whole genome sequencing was used for development of microsatellite markers so as to ensure availability of substantial number of polymorphic markers for use in marker assisted breeding programs. Whole genome sequencing of sesame variety ‘Swetha’ was done using Illumina paired-end sequencing and Roche 454 shotgun sequencing technologies (GCA_000975565.1 in GenBank). ‘GinMicrosatDb’, a genome-wide microsatellite marker database has been developed using the whole genome sequence data of sesame variety ‘Swetha’. The database consists of microsatellites localized on both linkage groups and scaffolds with their genomic co-ordinates. It provides five sets of forward and reverse primers for each of the microsatellite loci along with the flanking sequences, primer GC content, product size and melting temperature etc. The distribution of microsatellites can be viewed and selected through a genome browser as well as through a physical map. The newly identified microsatellite markers are expected to help sesame breeders in developing marker tags for traits of economic importance thereby bringing about greater efficiency in marker-assisted selection programs.