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The systematic literature review of 111 abstracts has been conducted to comprehensively compile the empirical studies of 21 complete text papers from all over the globe in context to estimation or determination of the technical efficiency (TE) at plantation level of tea production system (TPS), by adopting two methodologies viz., stochastic frontier analysis (SFA) and data envelopment analysis (DEA) during the period 2012-2022. Investigation from these empirical studies revealed that the average TE (TEmean) of tea growers (TG)s all around the globe computed by using both the approaches is around 67.98%, which showcased that the TGs have ability to increase the green tea leaf (GTL) production by 32.02% through better utilization of available resources and technology. The influence of various factors on TE of these TGs had contradictory outcomes, which broke new ground for future research. Computation of TE will enable an investigator to benchmark the best performing TGs in a particular area, which may be referred to by the inefficient TGs to enhance their performance.

Duck plague is an economically devastating viral disease prevalent in ducks which causes massive loss of duck populations annually. High morbidity and mortality are characteristic of the infection and outbreaks. Duck plague is reported from time to time from different parts of our country including Assam. The infected ducks manifest clinical signs like photophobia, partially or completely closing of eyelids, excessive thirst, nasal discharge, anorexia, drooped plumage, ataxia, diarrhea with soiled vents along with tremors in the head, neck and body. Present study was aimed to evaluate the deleterious effects of duck plague virus on liver and kidney condition by estimating liver and kidney specific biomarkers from indigenous ducks, namely Pati, Nageswari and Cinahanh of Assam from different agroclimatic zones. Study revealed significant increase of AST, ALT, ALP, uric acid and creatinine which is indicative of disruption of healthy function of liver and Kidney due to duck plague virus infection.

With the advent of newer methods and tools for accurate analysis, the outcomes of researches have become increasingly precise. This increase in reliability of research findings and forecasting is well demanded in various fields of science including those under animal breeding as well as its allied aspects such as production. One such recent breakthrough is an artificial neural network (ANN), which is a system of hardware and/or software programmed to function like the operation of neurons in the human brain. ANNs are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence. The determination of milk yield, quality of meat animals, predicting breeding values, inferring demography and recombination, genome-enabled prediction are few of the sectors where ANN has proved to be a valuable asset. The correlation coefficient between the experimental values and those predicted with the help of ANN was found to be close to 1. These studies were a significant indication of the impactful ability of ANN. The early selection of superior bulls, culling of low performing ones, frequent prediction of breeding values and taking into account the non linearity encountered in biological research are few of the many benefits of using ANN in researches of animal genetics and production. Hence, considering its prodigious role, this current review has been written to highlight the role and impact of ANN in livestock studies.