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.

Climate Change Risk Perception, Adaptation and Mitigation Strategy: An Extension Outlook in Mountain Himalaya

Abstract: Climate change is becoming an ever increasing global threat which is difficult to ignore. The major underlying cause is anthropogenic, i.e. excessive use of fossil fuels, destruction of forests for industrialisation and urbanisation with rapid overgrowing population. The danger is such alarming that ecosystem will be irreversibly altered which will lead to suffering of human life by many ways. The overriding appearance of climate change is the increasing average worldwide temperature which is popularly called as global warming, and as a consequence several regions of the Earth are facing visible problems such as melting of glaciers, sea level rising, deviations in precipitation patterns and increase in plant diseases, and a number of bourgeoning challenges for public health are coming across by many nations. According to the Intergovernmental Panel on Climate Change (IPCC) report, the Indian Himalayan ecosystem (IHE) is one of the extremely vulnerable zones followed by the coastal ecosystem towards the climate change in India, and as per projection the climate change will impart serious environmental, economic and social impacts of the Indian Himalaya agricultural production system. At this juncture, strong adaptation and mitigation strategy is needed for reducing the vulnerability of resource-poor hill farmers and sustainable development of the Himalayan ecosystem. Climate change adaptation involves holistic changes in agricultural and ecological management practices. It comprises a combination of distinct responses, the indigenous knowledge systems, alternative practices and accessible technologies. Adaptation policy should be taking into account the farmers’ perspective. In this piece of writing, the focus is to draw an outline of present condition and, furthermore, propose a strategy for effective adaptation and mitigation of climate change suited for Himalayan agricultural system.

Crosses with spelt improve tolerance of South Asian spring wheat to spot blotch, terminal heat stress, and their combination.

Abstract: Spot blotch and terminal heat are two of the most important stresses for wheat in South Asia. A study was initiated to explore the use of spelt (Triticum spelta) to improve tolerance to these stresses in spring wheat (T. aestivum). We assessed 185 recombinant inbred lines (RILs) from the cross T. spelta (H + 26) × T. aestivum (cv. HUW234), under the individual stresses and their combination. H + 26 showed better tolerance to the single stresses and also their combination; grain yield in RILs was reduced by 21.9%, 27.7% and 39.0% under spot blotch, terminal heat and their combined effect, respectively. However, phenological and plant architectural traits were not affected by spot blotch itself. Multivariate analysis demonstrated a strong negative correlation between spikelet sterility and grain yield under spot blotch, terminal heat and their combination. However, four recombinant lines demonstrated high performance under both stresses and also under their combined stress. The four lines were significantly superior in grain yield and showed significantly lower AUDPC than the better parent. This study demonstrates the potential of spelt wheat in enhancing tolerance to spot blotch and terminal heat stresses. It also provides comprehensive evidence about the expression of yield and phenological traits under these stresses.

Self Exciting Threshold Autoregressive Models for Describing Cyclical Data

Abstract: We thoroughly study a very important family of nonlinear timeseries models, viz. Self exciting threshold autoregressive (SETAR) types of models. A heartening feature of this family is that it is capable of describing cyclical data. As an illustration, SETAR models are then applied to country's lac export data during the period 1900-2000, obtained from Annual reports of Shellac Export Promotion Council, Kolkata. It is shown that fitted model, based on minimum Akaike information criterion (AIC), exhibits a threshold behaviour. Finally, attempts are made to obtain optimal predictor for out-of-sample data based on fitted SETAR model, which is found to be quite satisfactory.

Small area estimation of proportions with different levels of auxiliary data.

Abstract: Binary data are often of interest in many small areas of applications. The use of standard small area estimation methods based on linear mixed models becomes problematic for such data. An empirical plug-in predictor (EPP) under a unit-level generalized linear mixed model with logit link function is often used for the estimation of a small area proportion. However, this EPP requires the availability of unit-level population information for auxiliary data that may not be always accessible. As a consequence, in many practical situations, this EPP approach cannot be applied. Based on the level of auxiliary information available, different small area predictors for estimation of proportions are proposed. Analytic and bootstrap approaches to estimating the mean squared error of the proposed small area predictors are also developed. Monte Carlo simulations based on both simulated and real data show that the proposed small area predictors work well for generating the small area estimates of proportions and represent a practical alternative to the above approach. The developed predictor is applied to generate estimates of the proportions of indebted farm households at district-level using debt investment survey data from India.