Bayesian Statistics: Concepts and Applications in Animal Breeding – A Review

Statistics uses two major approaches- conventional (or frequentist) and Bayesian approach. Bayesian approach provides a complete paradigm for both statistical inference and decision making under uncertainty. Bayesian methods solve many of the difficulties faced by conventional statistical methods, and extend the applicability of statistical methods. It exploits the use of probabilistic models to formulate scientific problems. To use Bayesian statistics, there is computational difficulty and secondly, Bayesian methods require specifying prior probability distributions. Markov Chain Monte-Carlo (MCMC) methods were applied to overcome the computational difficulty, and interest in Bayesian methods was renewed. In Bayesian statistics, Bayesian structural equation model (SEM) is used. It provides a powerful and flexible approach for studying quantitative traits for wide spectrum problems and thus it has no operational difficulties, with the exception of some complex cases. In this method, the problems are solved at ease, and the statisticians feel it comfortable with the particular way of expressing the results and employing the software available to analyze a large variety of problems

Scientific Reports

Not AvailableWater footprint (WF), a comprehensive indicator of water resources appropriation, has evolved as an efficient tool to improve the management and sustainability of water resources. This study quantifies the blue and green WF of major cereals crops in India using high resolution soil and climatic datasets. A comprehensive modelling framework, consisting of Evapotranspiration based Irrigation Requirement (ETIR) tool, was developed for WF assessment

Bayesian Statistics: Concepts and Applications in Animal Breeding – A Review

Statistics uses two major approaches- conventional (or frequentist) and Bayesian approach. Bayesian approach provides a complete paradigm for both statistical inference and decision making under uncertainty. Bayesian methods solve many of the difficulties faced by conventional statistical methods, and extend the applicability of statistical methods. It exploits the use of probabilistic models to formulate scientific problems. To use Bayesian statistics, there is computational difficulty and secondly, Bayesian methods require specifying prior probability distributions. Markov Chain Monte-Carlo (MCMC) methods were applied to overcome the computational difficulty, and interest in Bayesian methods was renewed. In Bayesian statistics, Bayesian structural equation model (SEM) is used. It provides a powerful and flexible approach for studying quantitative traits for wide spectrum problems and thus it has no operational difficulties, with the exception of some complex cases. In this method, the problems are solved at ease, and the statisticians feel it comfortable with the particular way of expressing the results and employing the software available to analyze a large variety of problems

Comprehensive environmental impact assessment for designing carbon-cum-energy efficient, cleaner and eco-friendly production system for rice-fallow agro-ecosystems of South Asia

Not AvailableHigh energy consumption and carbon emission are the major components of environmental pollution. Reducing carbon-footprints and improving energy use efficiency in rice (Oryza sativa L.) - fallow production systems of South Asia is a great challenge. The present experiment was conducted for five consecutive years (2016?2020) with an aim to design the most carbon-cum-energy efficient, cleaner/safer and eco-friendly production systems for rice-fallows in eastern India. This split-plot experiment had crop establishment-cum-residue management (CERM) treatments in main-plots and post-rainy/winter season crops in sub-plots. The production systems selected for analysis included three crop establishment methods [(1) zero-till-direct-seeded rice (ZTDSR), (2) conventional-till direct-seeded rice (CTDSR), and (3) transplanted puddled rice (TPR)], and two residue management practices [(i) with residue, and (ii) without residue] in combination with five potential winter season crops i.e., chickpea (Cicer arietinum L.), lentil (Lens culinaris L.), safflower (Carthamus tinctorius L.), linseed (Linum usitatissimum L.), and mustard (Brassica juncea L.). Results revealed an increase in overall system productivity from 3.5 to 5.13 Mg ha?? 1 due to the diversification of rice-fallow systems with oilseed and pulse crops. Irrespective of residue management practices, ZTDSR increased the yield by 15 and 31% in chickpea, 15 and 34% in lentil, 33 and 50% in safflower, 9 and 19% in linseed, and 7 and 15% in mustard as compared to CTDSR and PTR, respectively. Moreover, adoption of ZTDSR reduced energy uses by 23.3%, while increased energy ratio and net returns by 14.3 and 10.9%, respectively, over TPR. Pulse based crop rotations (rice-lentil and rice-chickpea) under ZTDSR with surface crop residue yielded 21.5% higher system net returns as compared to rice-oilseed production systems. ZTDSR treatment also reduced carbon-footprint (C-footprint) by 2.8% compared to TPRbased production systems. Similarly, rice-oilseed systems had a 16.1% lower C-footprint in comparison to rice-pulse sequences. Hence, rice-chickpea, rice-lentil and rice-safflower production systems in combined with ZTDSR along with residue retention can be viable production systems with higher system productivity, better economic returns, higher energy ratio and lower C-footprint. These systems will ensure an efficient utilization of natural resources leading to long-term sustainability of the rice-fallow production systems of South Asia