Browsing by Author "Alok Kumar Pandey"

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A new grey system approach to forecast closing price of Bitcoin, Bionic, Cardano, Dogecoin, Ethereum, XRP Cryptocurrencies
(Springer Science and Business Media B.V., 2023) Pawan Kumar Singh; Alok Kumar Pandey; S.C. Bose
The current study uses the grey forecasting model, EGM (1, 1, α, θ), a generalized form of the classical, even form of grey forecasting approach, to forecast the closing price of Bitcoin (BTC), Bionic (BNC), Cardano (ADA), Dogecoin (DOGE), Ethereum (ETH), XRP (XRP) of cryptocurrencies based on the data from September 19, 2021, to September 29, 2021. The forecast was generated for September 30, 2021–October 07, 2021. Study revealed that the generalized model’s forecast accuracy is generally better than that of the classical model. The results are also compared with Linear Regression and Exponential Regression. This superiority results from using real past data in long-term forecasting, while the iterative forecasting approach uses the predicted values. Since forecast values are important in guiding future investments, decision-makers must consider various forecasting methods and select the best forecast performance after analyzing the comparative performance. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.
A SWOT analysis of groundnut farm households: Evidence from Mirzapur district in India
(Agricultural Academy, Bulgaria, 2021) Mosab I. Tabash; Pawan Kumar Singh; Rajiv Kumar Bhatt; Alok Kumar Pandey
In this study, SWOT analysis has been used to understand the safety precautions, cost-benefit measures, farmers’ skill, and few eco-friendly indicators. Generally, socio-economic characteristics of an agriculture farm community illustrate pro-duction, investment, educational status, farmers’ skill, their lifestyle, and the overall development scenario and prospects. To understand the status of these indicators a cross-sectional study was conducted in the Sikhar block of Mirzapur District, Uttar Pradesh, India. Data was collected through face to face interviews with the help of a pre-structured schedule. This work has found farmers’ awareness and application of herbicides are the major strengths in solving existing issues. Weaknesses include lack of latest information, skill in farms households, and inconsistencies in the application of a divergent range and scientific suggestions. Opportunities include alignment with farmers using strategies and existing tools with the application of herbicides as cost-effective and also helpful in reducing irrigation requirements. Threats consider that the application of herbicides to remove unwanted plants is one of the best and more effective but it should be applied in judicial form and farm households should have proper training and scientific recommendations about its application procedure. The present study will help in making strategies for farm households to improving decision-making to solve major issues, but also widely interpreting and communicating to select cost-effective methods. Policymakers should also be careful about weaknesses and it can be mini-mized by facilitating with proper infrastructure, awareness campaigns, interdisciplinary research, and institutional support to farm households. © 2021, Agricultural Academy, Bulgaria. All rights reserved.
An empirical investigation of inequality among the self employed women entrepreneurs in informal sector in India
(Inderscience Publishers, 2020) Annapurna Dixit; Alok Kumar Pandey
The present study takes into account self employed women entrepreneurs engaged in running vegetable/fruit/flower/fish, beauty parlour, boutique, stationary/gift/toy, general store, cosmetic/bangle, grocery store, tea/cold drink/egg/pan, clothes/garment and others. Based on 935 women entrepreneurs Gini coefficient and Theil index have been the highest for those women entrepreneurs who are engaged in tea/cold drink/egg/pan business and the lowest for those engaged in vegetable/fruit/flower/fish. Subgroup indices of general entropy and Atkinson for economic activities shows that GE (-1) is the highest for and district wise general stores, GE (0) is the highest for beauty parlour, GE (1) is the highest tea/cold drink/egg/pan and GE (2) is the highest for cloth/garments. Subgroup indices of Atkinson A (0.5), A (1) and A (2) for economic activities are the highest for tea/cold drink/egg/pan business, vegetable/fruit/flower/fish and for beauty parlour business. It is worth mentioning here that within group inequality is more than the between group inequality. © 2020 Inderscience Enterprises Ltd.
Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India
(MDPI, 2023) Ansari Saleh Ahmar; Pawan Kumar Singh; R. Ruliana; Alok Kumar Pandey; Stuti Gupta
The agriculture sector plays an essential function within the Indian economic system. Food-grains provide almost all the calories and proteins. This paper aims to compare ARIMA, SutteARIMA, Holt-Winters, and NNAR models to recommend an effective model to predict foodgrains production in India. The execution of the SutteARIMA predictive model used in this analysis was compared with the established ARIMA, Neural Network Auto-Regressive (NNAR), and Holt-Winters models, which have been widely applied for time series prediction. The findings of this study reveal that both the SutteARIMA model and the Holt-Winters model performed well with real-life problems and can effectively and profitably be engaged for food grain forecasting in India. The food grain forecasting approach with the SutteARIMA model indicated superior performance over the ARIMA, Holt-Winters, and NNAR models. Indeed, the actual and predicted values of the SutteARIMA and Holt-Winters forecasting models are quite close to predicting foodgrains production in India. This has been verified by MAPE and MSE values that are relatively low with the SutteARIMA model. Therefore, India’s SutteARIMA model was used to predict foodgrains production from 2021 to 2025. The forecasted amount of respective crops are as follows (in lakh tonnes) 1140.14 (wheat), 1232.27 (rice), 466.46 (coarse), 259.95 (pulses), and a total 3069.80 (foodgrains) by 2025. © 2023 by the authors. Licensee MDPI, Basel, Switzerland.
COVID-19 pandemic and transmission factors: An empirical investigation of different countries
(John Wiley and Sons Ltd, 2021) Pawan Kumar Singh; Ravi Kiran; Rajiv Kumar Bhatt; Mosab I. Tabash; Alok Kumar Pandey; Anushka Chouhan
The present work evaluates the impact of age, population density, total population, rural population, annual average temperature, basic sanitation facilities, and diabetes prevalence on the transmission of COVID-19. This research is an effort to identify the major predictors that have a significant impact on the number of COVID-19 cases per million population for 83 countries. The findings highlight that a population with a greater share of old people (aged above 65) shows a higher number of COVID-19 positive cases and a population with a lower median age has fewer cases. This can be explained in terms of higher co-morbidities and the lower general immunity in the older age group. The analysis restates the widely seen results that a higher median age and greater prevalence of co-morbidities leads to higher cases per million and lesser population density and interpersonal contact helps in containing the spread of the virus. The study finds foundation in the assertion that a higher temperature might lower the number of cases, or that temperature in general can affect the infectivity. The study suggests that better access to sanitation is a certain measure to contain the spread of the virus. The outcome of this study will be helpful in ascertaining the impact of these indicators in this pandemic, and help in policy formation and decision-making strategies to fight against it. © 2021 John Wiley & Sons, Ltd
Eastern Son Valley, Uttar Pradesh, India: a Case for IUGS Geological Heritage Site Designation
(Springer, 2022) Gurmeet Kaur; Swtantra Kumar Singh; Prabhakar Upadhyay; Parminder Kaur; Jaspreet Saini; Alok Kumar Pandey; Meenal Mishra
The part of Eastern Son valley of India displays a unique geographical location and geology endowed with diversity, distinctiveness and uniqueness. This region is significantly rich in socio-cultural, historical, spiritual and natural heritage. The Salkhan Fossil Park, the Kaimur sandstone terrane, numerous waterfalls, water reservoirs and geological features together represent the gorgeous landscape that evolved since Palaeoproterozoic. The mountainous region is also marked by a number of historical forts, buildings, rock shelters, prehistoric cave paintings, captivating wildlife and rich mineral resources. Son Echo Point is a significant geotourism site located on the steep scarp of Markundi hill in the Sonbhadra district. It has been developed as the consequence of the Markundi–Jamwal Fault. From Son View Point, one can enjoy a panoramic and breathtaking/spectacular view of Son valley carved along the Son–Narmada lineament. The Markundi hills become a popular tourist destination during the monsoon season because of the stunning landscape and numerous seasonal waterfalls. Another major geotourism location is Veer Lorik Pathhar (stone), which is located on Markundi hill around 100 m from Son Echo Point. In addition to the geologists’ point of view of its origin, it is also shrouded in captivating local folklore. The antiquity of human activity in this area goes back to the Lower Palaeolithic period. Numerous archaeological sites, such as cave monuments and cave paintings, have been uncovered, depicting the cultural and religious beliefs of the local population. Archaeological remains in the form of tools and pottery have been discovered in a number of locations throughout the region. The region is important for its geology as well as for the sociocultural beliefs of the local population. We suggest that the eastern Son valley be recognised as an IUGS global geological heritage site from India based on the noteworthy geoheritage and geotourism sites. © 2022, The Author(s), under exclusive licence to International Association for the Conservation of Geological Heritage.
Exploring Livestock and Agricultural Income Poverty among Farming Households: Study Based on Mirzapur District of Uttar Pradesh
(Indian Society of Agricultural Economics, 2024) Alok Kumar Pandey
This paper investigates poverty indices related to livestock and crop income among farming households. Data was collected from 400 households in the Mirzapur district. The study highlights the significant role of livestock ownership in poverty reduction, rural development, and food security, with women often managing livestock, contributing to their socio-economic empowerment. Various poverty indices, such as Headcount ratio, Poverty gap ratio, and Income gap ratio, were calculated for households with and without livestock. Results show that households with livestock earn around 21 per cent of their income from livestock and 73 per cent from crops, while households without livestock derive 89 per cent of their income from crops alone. Poverty indices are lower for households with livestock than those without, indicating that livestock ownership reduces poverty severity. The findings emphasize the need for policies promoting sustainable livestock production systems to alleviate poverty, particularly among small and marginal farmers. Providing access to subsidized livestock breeds and enhancing rural livestock promotion could contribute to higher incomes and employment generation in rural areas. © 2024 Indian Society of Agricultural Economics. All rights reserved.
Forecasting global plastic production and microplastic emission using advanced optimised discrete grey model
(2023) Subhra Rajat Balabantaray; Pawan Kumar Singh; Alok Kumar Pandey; Bhartendu Kumar Chaturvedi; Aditya Kumar Sharma
Plastic pollution has become a prominent and pressing environmental concern within the realm of pollution. In recent times, microplastics have entered our ecosystem, especially in freshwater. In the contemporary global landscape, there exists a mounting apprehension surrounding the manifold environmental and public health issues that have emerged as a result of the substantial accumulation of microplastics. The objective of the current study is to employ an enhanced grey prediction model in order to forecast global plastic production and microplastic emissions. This study compared the accuracy level of the four grey prediction models, namely, EGM (1,1, α, θ), DGM (1,1), EGM (1,1), and DGM (1,1, α) models, to evaluate the accuracy levels. As per the estimation of the study, DGM (1,1, α) was found to be more suitable with higher accuracy levels to predict microplastic emission. The EGM (1,1, α, θ) model has slightly better accuracy than the DGM (1,1, α) model in predicting global plastic production. Various accuracy measurement tools (MAPE and RMSE) were used to determine the model's efficiency. There has been a gradual growth in both plastic production and microplastic emission. The current study using the DGM (1,1, α) model predicted that microplastic emission would be 1,084,018 by 2030. The present study aims to provide valuable insights for policymakers in formulating effective strategies to address the complex issues arising from the release of microplastics into the environment and the continuous production of plastic materials. © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Forecasting of non-renewable and renewable energy production in India using optimized discrete grey model
(Springer Science and Business Media Deutschland GmbH, 2023) Alok Kumar Pandey; Pawan Kumar Singh; Muhammad Nawaz; Amrendra Kumar Kushwaha
Renewable energy delivers reliable power supplies and fuel diversification, enhancing energy security and lowering fuel spill risk. Renewable energy also helps conserve the nation’s natural resources. Solar and other renewable energy sources have become increasingly prominent in recent years. India has achieved the 20 GW capacity solar energy production target before 2022. It is presently producing the lowest-cost solar power at the global level. Thermal energy has dominated the energy market. Countries have decided on energy generation from renewable sources and adopting green energy. This study forecasted non-renewable and renewable energy from multiple sources (hydropower, solar, wind and bioenergy) using grey forecasting model DGM (1,1,α). The comparative analyses with the classical models DGM (1,1) and EGM (1,1) revealed the superiority of the DGM (1,1,α). We also used CAGR for 2009–2019 to compare the actual and predicted data growth rate. The results show that non-renewable and renewable energy production is expected to increase. However, renewable energy generation wind sources continue to increase faster than hydropower, solar and bioenergy. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Impact of pandemic on development and demography in different continents and nations
(John Wiley and Sons Ltd, 2023) Pawan Kumar Singh; Alok Kumar Pandey; Ravi Kiran; Rajiv Kumar Bhatt; Anushka Chouhan
This study has collected information of 145 countries to predict the effect of cases per million (CPM), tests per million (TPM), and proportion of people aged 65 and above (PAO) on the number of deaths per million DPM at the country and continent level. In addition, it evaluates the economic cost of tests, deaths, COVID-19 cases in terms of reduction in GDP growth rate across the countries. This paper uses a different econometrics model, including analysis of variance (ANOVA), regression, and multinomial regression model. The robust regression model with M and MM-estimation was also used due to leverage and residuals in country wise GDP database. A significant difference was found in deaths per million (DPM), TPM, number of COVID-19 cases (CPM), and percentage of people aged 65 and above (PAO) across continents. The DPM is negatively associated with TPM, and it was relatively more effective in reducing DPM in Africa (0.32%) as compared to Asia (0.25%) and Europe (0.28). The results show that a 1% increase in the elderly population causes a 0.62% increase in DPM in Africa, while it caused a 2.31% increase in Europe. The study will be helpful in ascertaining the impact of these indicators in this pandemic and help in policy formation and decision-making strategies to fight the COVID19 pandemic. © 2021 John Wiley & Sons Ltd.
Isolation and characterization of a novel nitrogen fixer Beijerinckia fluminensis strain BAUMS11 from litchi (Litchi chinensis L.) rhizosphere
(Applied and Natural Science Foundation, 2024) Mahendra Singh; Santosh Kumar; Dhirendra Kumar Singh; Tushar Ranjan; Alok Kumar Pandey
Indiscriminate use of mineral fertilizers has a broad negative impact on soil health. Because of the above, there is an urgent need to search for natural organic alternatives, including using soil microbial resources to replenish soil nutrients for enhanced Agri productivity vis a vis sustainably maintaining soil health. The nitrogen-fixing rhizobacteria (NFR) are such type of bacteria which fix gaseous atmospheric nitrogen in the soil and in nodules of certain plant species in considerable amounts that are readily available for plants' uptake and may be considered as a viable alternative source of mineral nitrogen application. The present study was conducted to isolate the most potent nitrogen-fixing bacteria from the litchi rhizosphere. Hence, Five NFR (NFR1 to NFR5) were isolated from the rhizosphere of litchi orchard of Bihar Agricultural University, Sabour, Bhgalpur, India, based on their ability to fix atmospheric nitrogen in a nitrogen-free mineral salt medium. NFR2 was found to be the most potent in fixing atmospheric nitrogen (11.31 mg N per gram carbon source)among all the isolated rhizobacterial strains. Hence, on the basis of biological nitrogen fixation ability, the isolate NFR2 was subjected to 16S ribosomal RNA (16S rRNA) gene sequencing for molecular characterization. Based on 16S rDNA sequence analysis, NFR2 showed the closest sequence homology with Beijerinckia fluminensis and was identified and reported as Beijerinckia fluminensis strain BAUMS11, Accession number MN533953. The study noticeably indicated that the B. fluminensis strain BAUMS11 was found most efficient in fixing gaseous atmospheric nitrogen and may be used for the manufacturing of nitrogenous biofertilizer, which can fix atmospheric nitrogen to the tune-up to 30 kg N ha-1yr-1. © Author (s).
Multiple forecasting approach: a prediction of CO2 emission from the paddy crop in India
(Springer Science and Business Media Deutschland GmbH, 2022) Pawan Kumar Singh; Alok Kumar Pandey; Sahil Ahuja; Ravi Kiran
This paper compares four prediction methods, namely random forest regressor (RFR), SARIMAX, Holt-Winters (H-W), and the support vector regression (SVR), to forecast the total CO2 emission from the paddy crop in India. The major objective of this study is to compare these four models and suggest an effective model for the prediction of total CO2 emission. Data from 1961 to 2018 has been categorised into two parts: training and test data. The study forecasts total CO2 emission from paddy crops in India from 2019 to 2025. A comparison of mean absolute percentage error (MAPE) and the mean square error (MSE) highlights the differences in accuracy among the four models. The mean absolute percentage eror (MAPE) and the mean square error (MSE) for the four methods are RFR (MAPE: 5.67; MSE: 549,900.02), SARIMAX (MAPE: 1.67; MSE:70,422.35), H-W (MAPE:0.75; MSE:16,648.58), and SVR (MAPE: 0.91; MSE: 17,832.4). The values of MAPE and MSE with the Holt-Winters (H-W) and the support vector regression (SVR) are relatively low as compared to SARIMAX and RFR. Based on these results, it can be inferred that H-W and SVR were found suitable models to forecast the total CO2 emission from paddy crops. Holt-Winters model predicted 14,364.97 for the year 2025, and SVR predicted 13,696.67 for the year 2025. The decision-maker can use these predictions to build a suitable policy for the future. This approach can be contrasted with other forecasting methods, such as the neural network, and train the model to achieve better forecast accuracy. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Prediction of surface temperature and CO2 emission of leading emitters using grey model EGM (1,1, α, θ)
(Springer Science and Business Media Deutschland GmbH, 2023) Pawan Kumar Singh; Alok Kumar Pandey; Anushka Chouhan; Gopal Ji Singh
The current study projects the increase in surface temperature and CO2 emissions using the EGM (1,1, α, θ) grey model for the six most significant CO2 contributing countries, namely China, the USA, India, Russia, Japan, and Germany. The study uses time series data for surface temperature (in degree celsius) from 2010 to 2020, and CO2 emission (metric tons per capita) data from 2009 to 2019. The empirical results show a downward trend in CO2 emissions from Japan, Germany, the USA, and Russia by 2028. However, in the same time period, CO2 emissions are expected to increase for India and remain nearly constant for China. This study indicates an increase in surface temperature at a significant rate in all the 6 countries: by 6.70 °C for China, 7.52 °C for Germany, 2.95 °C for India, 2.66 °C for Japan, 3.61 °C for Russia, and 13.48 °C for the USA by the end of 2028. The study compares the EGM (1,1, α, θ) grey model with the general EGM (1,1) grey model and finds that the EGM (1,1, α, θ) model performs better in both in-sample and out-of-sample forecasting. The paper also puts forward policy suggestions to mitigate, manage, and reduce increases in surface temperature as well as CO2 emissions. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Spatio-temporal variability analysis of evapotranspiration, water use efficiency and net primary productivity in the semi-arid region of Aravalli and Siwalik range, India
(Springer Science and Business Media B.V., 2023) Shubham Kumar; Ritesh Kumar; Manoj Kumar; Alok Kumar Pandey; Prashant K. Srivastava; Sanchit Kumar; Varun Narayan Mishra; V.S. Arya
Sustainable and effective water use management is a global challenge for optimum productivity for all types of vegetation cover. Evapotranspiration (ET) is one of the key components determining the soil moisture conditions ensuring the water availability for vegetation in an area. The present study provides a strong basis for existing water conditions in the study area using evapotranspiration as an important tool. There is a large variability in evapotranspiration during the different months of the year 2022–23 as well as over a long period of study ranging from the year 2001 to 2022. Water use efficiency in the study area is 0.32 g C/kg H2O which is less than half that of China and USA. The study showed an increasing trend of net primary productivity (NPP) and water use efficiency (WUE) during 2001–22. The comparatively lower WUE and NPP in comparison with global average are of great concern for a semi-arid region, which is also India’s leading agricultural producer state. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.