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.

Stochastic Modelling of Monthly Rainfall Volume during Monsoon Season over Gangetic West Bengal, India

Abstract: The aim of the present study is to find suitable probability distributions which describe best the monthly rainfall volume during monsoon (MRM) for each of the thirteen districts which constitute Gangetic West Bengal (GWB) meteorological sub-division of India For this purpose, 18 continuous probability distributions are employed to examine the fitness of time series data of 113 years (1901-2013) The parameters of the distributions are estimated either by maximum likelihood, moments or L-moments method Based on goodness of fit (GOF) tests viz, Kolmogorov-Smirnov, Anderson-Darling and Chi-Squared, three separate rankings are given to each of the distributions for all the districts Further, a method of scoring, based on ranking, has been adopted to pick up the best fitted distribution for each district Log-Logistic (LL) distribution comes out as the most suitable for five districts whereas, for a particular district, Generalized Extreme Value (GEV) and Pearson 5 (P5) distributions jointly rank first among all the distributions Individually, P5 explains best the monthly rainfall of two districts while GEV is found to be most suitable for another district Log-Pearson 3 (LP3), 3-parameter Dagum (D3), 4-parameter Generalized Gamma (GG4) and 3-parameter Generalized Gamma (GG3) are the others which are found to be most candidate distributions for one district each Finally, MRM, for all the districts, is estimated for different return periods Probability of occurrences of MRM, at various points of exceedance, has also been calculated

Hierarchical Bayes estimation of small area means under a spatial nonstationary Fay–Herriot model

Abstract: The Fay–Herriot (FH) model is widely used in small area estimation (SAE) for aggregated level data, but in several applications presence of spatial effect between contiguous or neighboring region c...

A Novel Approach for Development and Evaluation of LiDAR Navigated Electronic Maize Seeding System Using Check Row Quality Index

Abstract: Crop geometry plays a vital role in ensuring proper plant growth and yield. Check row planting allows adequate space for weeding in both direction and allowing sunlight down to the bottom of the crop. Therefore, a light detection and ranging (LiDAR) navigated electronic seed metering system for check row planting of maize seeds was developed. The system is comprised of a LiDAR-based distance measurement unit, electronic seed metering mechanism and a wireless communication system. The electronic seed metering mechanism was evaluated in the laboratory for five different cell sizes (8.80, 9.73, 10.82, 11.90 and 12.83 mm) and linear cell speed (89.15, 99.46, 111.44, 123.41 and 133.72 mm·s−1). The research shows the optimised values for the cell size and linear speed of cell were found to be 11.90 mm and 99.46 mm·s−1 respectively. A light dependent resistor (LDR) and light emitting diode (LED)-based seed flow sensing system was developed to measure the lag time of seed flow from seed metering box to bottom of seed tube. The average lag time of seed fall was observed as 251.2 ± 5.39 ms at an optimised linear speed of cell of 99.46 mm·s−1 and forward speed of 2 km·h−1. This lag time was minimized by advancing the seed drop on the basis of forward speed of tractor, lag time and targeted position. A check row quality index (ICRQ) was developed to evaluate check row planter. While evaluating the developed system at different forward speeds (i.e., 2, 3 and 5 km·h−1), higher standard deviation (14.14%) of check row quality index was observed at forward speed of 5 km·h−1.

A stochastic logistic model with time delay

Abstract: Summary The stochastic version of the logistic model for population growth is generalized to take account of continuously distributed time delay with an exponentially decaying kernel. The theory of diffusion processes is used to analyse the probability density function of the population size. The explicit expression for the stationary distribution is worked out and the effect of time delay on various statistics is discussed.

Nonlinear regression techniques for analysis of onion (Allium cepa) production in India

Abstract: India occupies second position in the production of onion in the world. Keeping in view the importance of this crop, the present study has been undertaken to find out the growth in production of onion and discusses the application of nonlinear models, viz. Gompertz, Logistic, MMF, Richards and Weibull models, which measure the growth. Time series data on onion production in major growing states; viz. Andhra Pradesh, Gujarat, Karnataka, Maharashtra, Uttar Pradesh, and all India from 1990–91 to 2009–10 has been utilized for the present study. From a realistic point of view, the relationships among variables in agricultural and horticultural sciences are non-linear in nature. Non-linear models are very popularly used to estimate the trend in various fields such as population studies and animal growth where growth is not symmetrical about the point of inflection. The results showed that Logistic and Gompertz models faired marginally better than Weibull and MMF models