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.

Wavelet Frequency Domain Approach for Statistical Modeling of Rainfall Time-Series Data

Abstract: The powerful methodology of “Wavelet analysis in frequency domain” for analyzing time-series data is studied. As an illustration, Indian monsoon rainfall time-series data from 1879–2006 is considered. The entire data analysis is carried out using SPLUS WAVELET TOOLKIT software package. The discrete wavelet transform (DWT) and multiresolution analysis (MRA) of the data are computed to analyze the behaviour of trend present in the time-series data in terms of different times and scales. By using bootstrap method, size and power of the test for testing significance of trend in the data is computed. It is found that the size of the test for Daubechies wavelet is more than that for Haar wavelet. In respect of both Daubechies and Haar wavelet filters, it is found that the test for presence of trend is unbiased. Also, power of the test for both Daubechies (D4) and Haar wavelets, at level 5 is less than the one at level 6. Further, Haar wavelet at level 6 has generally performed better than Daubechies (D4) wavelet at level 6 in terms of power of the test. Using the former wavelet, a declining trend in the data under consideration is revealed.

Forecasting maize yield using ARIMA-Genetic Algorithm approach:

Abstract: Maize is widely cultivated throughout the world and has highest production among all the cereals. India is the sixth largest producer of maize in the world, contributing 2% of global production and...

Development of Hybrid Models for Forecasting Time-Series Data Using Nonlinear SVR Enhanced by PSO

Abstract: Real-world time-series are rarely purely linear or nonlinear and often contain both these patterns. Therefore, in this article, we have developed four hybrid models by combining linear seasonal autoregressive integrated moving average (SARIMA) and nonlinear support vector regression (NLSVR) models for time-series forecasting. Further, particle swarm optimization (PSO), which is a very efficient population-based global stochastic optimization technique, is employed to estimate the hyperparameters of resultant models. A relevant computer program is written in MATLAB function (m file). The SAS and MATLAB software packages are used for carrying out data analysis. Subsequently, as an illustration, the models are applied to all-India monthly marine products export time-series data. Superiority of hybrid models over individual SARIMA and NLSVR models is demonstrated for the data under consideration using root mean square error (RMSE) and mean absolute error (MAE) criteria.

Soil organic carbon stock variability in the Northern Gangetic Plains of India: interaction between agro‐ecological characteristics and cropping systems

Abstract: Soil organic carbon (SOC) content and its spatial distribution in the Northern Gangetic Plain (NGP) Zone of India were determined to establish the cause–effect relationship between agro-ecological characteristics, prevailing crop management practices and SOC stock. Area Spread Index (ASI) approach was used to collect soil samples from the NGP areas supporting predominant cropping systems. Exponential ordinary kriging was found most suitable geo-statistical model for developing SOC surface maps of the NGP. Predicted surface maps indicated that 43.7% area of NGP had 0.5–0.6% SOC, while the rest of the area was equally distributed with high (0.61–0.75%) and low ( 12.5 Mg/ha) in 97.8, 57.6 and 46.4% areas of BTZ, CPZ and MWPZ, respectively. Only 9.8 and 0.4% area of WPZ and SWPZ, respectively, had SOC stock above the threshold value. The variation in SOC stock was attributed largely to carbon addition through recycling of organic sources, cropping systems, tillage intensity, crop or residue cover and land-use efficiency, nutrient-use pattern, soil texture and prevailing ecosystem. Adoption of conservation agriculture, balanced use of nutrients, inclusion of legumes in cropping systems and agro-forestry were suggested for enhancing SOC stock in the region.

An algorithmic approach to construct D-optimal saturated designs for logistic model

Abstract: In this paper, locally D-optimal saturated designs for a logistic model with one and two continuous input variables have been constructed by modifying the famous Fedorov exchange algorithm. A satur...