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.

Development of model web-server for crop variety identification using throughput SNP genotyping data

Abstract: Crop varieties or genotypes of a given species are pivotal for agricultural production and ownership, management and improvement of their germplasm is a great challenge. Its morphological identification requires time, cost and descriptors are often compromised statistically due to phenotypic plasticity. Development of DNA based signature of varieties can overcome these limitations. There is a global need to implement world trade organization (WTO) and intellectual property rights (IPR) guidelines of Plant Breeders Rights (PBR) where DUS (distinctness, uniformity and stability) testing can be supplemented by DNA profile. Universalization and minimization of SNP number without compromising identification accuracy is the major challenge in development of varietal profile by rapid genotype assay. Besides this, there is no server-based approach reducing computational skill with global accessibility of referral phenotypic and genotypic data. We report world’s first model web server for crop variety identification using >350 Indian wheat varieties and Axiom 35 K SNP chip data. Standard filtering and linkage disequilibrium approach were used to develop varietal signature in Linux using HTML, Java, PHP and MySQL with provision of QR code generator to facilitate bar-coding. Phylogenetic tree constructed by selected SNPs confirms six major trait based clusters of varieties and their pedigree. Our user friendly server based tool, VISTa (Variety Identification System of Triticum aestivum) ( http://webtom.cabgrid.res.in/vista ) can be used in DUS testing having dispute resolution of sovereignty and access benefit sharing (ABS) issues. This model approach can be used in other crops with pan-global level management of crop germplasm in endeavour of crop productivity.

Statistical Models for Forecasting Mango and Banana Yield of Karnataka, India

Abstract: Horticulture sector plays a prominent role in economic growth for most of the developing countries. India is the largest producer of fruits and vegetables in the world next only to China. Among the horticultural crops, fruit crops are cultivated in majority of the area. Fruit crops play a significant role in the economic development, nutritional security, employment generation, and overall growth of a country. Among fruit crops, mango and banana are largest producing fruits of India. Generally, Karnataka is called as the horticultural state of India. In Karnataka, mango and banana are highest producing fruit crops. With these prospective, yield of mango and banana of Karnataka have been chosen as study variables. Forecasting is a primary aspect of developing economy so that proper planning can be undertaken for sustainable growth of the country. In this study, classes of linear and nonlinear, parametric and non-parametric statistical models have been employed to forecast yield of mango and banana of Karnataka. The major drawback of linear models is the presumed linear form of the model. In most of the cases, the time series are not purely linear or nonlinear as they contain both linear and nonlinear components. To overcome this problem a hybrid model has been proposed which consists of linear and nonlinear models. The hybrid model with the combination of Autoregressive Integrated Moving Average (ARIMA) and Support Vector Regression model performed better in both model building as well as in model validation as compared to other models.