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As per the vision of promoting agricultural collectives, the government of India promoted the farmer producer organization (FPO). However, with the fast growth of FPOs, there is an issue with performance measurement. This study is aimed at the development of performance metrics for the FPOs.

In the first stage, we selected the measures from a secondary literature review and identified 11 parameters. Further, the Delphi round was conducted in the second stage with 26 experts working with FPOs and they were asked to rank these parameters. Based on the weightage of each parameter, the most important parameters were decided. The mean ranks and deviations of the performance parameters were analyzed. The hypothesis test and Kendall’s coefficient of concordance have been further used to validate the performance parameters. In the third stage, based on the inputs from the experts, a questionnaire was designed, and the data was collected from chief executive officers (CEOs) of the FPOs to identify the most important performance parameters.

The experts identified governance, financial support and professional management as important measures for FPOs. In the second round of the study, finance and governance were identified as the most important factors. It is important to note that finance and governance were the two most important factors in making an FPO successful. Finally, a 100-point metric was developed in seven major heads.

This study will be advantageous for all the stakeholders involved in the promotion of FPOs, including FPOs themselves, funding agencies providing funds to FPOs, skill-building organizations, etc.

This paper is one of its kind to develop a 100 points metrics for performance evaluation of FPOs.

The global outbreak of COVID-19 and its subsequent varied impacts on different economic activities necessitate to examine its disruptions and impacts on livestock sector in India due to its recently surging potential as an unrivaled alternative to boost farmer’s income.

The studies for review were identified through search in different databases using relevant keywords. Only full text papers written in English language were reviewed. The review was organized and streamlined using Covidence software.

Analysis of the literature reveals adverse effects of COVID-19 on functioning of input and output stages of livestock supply chains. This has resulted in upstream and downstream economic losses that affect livelihoods of the producers.

Scale of unprecedented crisis due to COVID-19 pandemic requires creative policy decisions to make livestock production systems robust, resilient and sustainable. Organized production systems are required to integrate with livestock-tech startups to modernize their supply chains, whereas local supply chains are required to reorient with government’s intervention in terms of developing on-farm production and postproduction processing facilities.

Although there exist some evidence on COVID-19-related impacts on livestock sector of India, but an integrated review of evidence on COVID-19 related disruptions at all the stages (from input supply to marketing) of livestock supply chains was missing.

Diabetes is one of the life-threatening chronic diseases, which is already affecting 422m people globally based on (World Health Organization) WHO report as at 2018. This costs individuals, government and groups a whole lot; right from its diagnosis stage to the treatment stage. The reason for this cost, among others, is that it is a long-term treatment disease. This disease is likely to continue to affect more people because of its long asymptotic phase, which makes its early detection not feasible.

In this study, the authors have presented machine learning models with feature selection, which can detect diabetes disease at its early stage. Also, the models presented are not costly and available to everyone, including those in the remote areas.

The study result shows that feature selection helps in getting better model, as it prevents overfitting and removes redundant data. Hence, the study result when compared with previous research shows the better result has been achieved, after it was evaluated based on metrics such as F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve. This discovery has the potential to impact on clinical practice, when health workers aim at diagnosing diabetes disease at its early stage.

This study has not been published anywhere else.