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Invasive plant species are considered one of the significant drivers of habitat loss, leading to biodiversity loss. They have also been observed to alter the local ecology, resulting in a decline of native flora. The management of invasive species is widely recognised as one of the most severe challenges to biodiversity conservation. The International Union for Conservation of Nature (IUCN) considers Lantana camara, as one of the ten worst weeds. Over time, native and indigenous species may evolve to co-exist or compete with invasive species, reducing invader fitness. It is observed that species competition fluctuates throughout environmental gradients, life phases, and abundances. Hence, competition outcome is very context-dependent. To address this challenge, we conducted a comprehensive study in three phases: we identified native species coexisting with Lantana in their natural habitats in the Doon Valley (Phase I) and documented the phenotypic traits of selected coexisting species using the Landmark BBCH (Biologische Bun-desantalt, Bundessortenamt und Chemische Industrie) scale, revealing the phenological growth patterns of selected co-existing species (Phase II). This was followed by conducting pot (Phase IIIa) and field (Phase IIIb) experiments to study the interactions between them. Notably, Justicia adhatoda , Broussonetia papyrifera , Pongamia pinnata , Urtica dioica and Bauhinia variegata demonstrated promising results in both pot and field conditions. Furthermore, after the mechanical removal of Lantana and prior to the plantation in the field experiments, four native grass species were introduced using the seed ball method. Among these, Pennisetum pedicellatum and Sorghum halpense exhibited prompt regeneration and effectively colonised the field, densely covering the cleared area. The study provides a comprehensive management plan for the restoration of Lantana affected areas through competition using native species. This study utilizes phenological assessment for native plant selection using reclamation from native grasses and proposes a management plan for combating invasive Lantana .

The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results. The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.

Intelligent data analytics for terror threat prediction is an emerging field of research at the intersection of information science and computer science, bringing with it a new era of tremendous opportunities and challenges due to plenty of easily available criminal data for further analysis.

The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections.

The comprehensive use of organic, inorganic, and biological components of nutrient management in rice ecologies can potentially address the twin challenges of declining factor productivity and deteriorating soil health. A field study was thus conducted at Varanasi, India during the year 2013–14 and 2014–15 to assess the effect of the recommended dose of fertilizers (RDF) along with cow product (blends of 5 cow by-products i.e., dung, ghee, curd, urine, and milk that is known as panchagavya ) on soil health, energy budget, and rice productivity. The results revealed that the inclusion of panchagavya as seedling root dip + 6% spray at 30 days after transplanting (DAT) + an application with irrigation water (15 l ha −1 ) at 60 DAT (D 4 ) along with 100% RDF (F 3 ) noted significantly higher rice grain yield (6.34 t ha −1 ) and higher dehydrogenase activity. However, the soil bacterial and actinomycetes population, soil microbial biomass carbon (SMBC), urease, and alkaline phosphatase activities were significantly higher with D 4 along with 120% RDF (F 4 ). Carbon output (5,608 kg CO 2 eq ha −1 ), energy use parameters viz . energy output (187,867 MJ ha −1 ), net energy returns (164,319 MJ ha −1 ), and energy intensity valuation (5.08 MJ x) were significantly higher under F 4 . However, the energy ratio (8.68), energy productivity (0.292 kg MJ −1 ), and energy profitability (7.68) remained highest with 80% RDF (F 2 ), while the highest carbohydrate equivalent yield (4,641 kg mha −1 ) was produced under F 3 . The combination of F 3 with D 4 resulted in the highest productivity, optimum energy balance, and maintaining soil quality. Therefore, a judicious combination of cow product ( panchagavya) with RDF was found to improve the rice productivity, energy profitability, and soil quality under south Asian eastern Indo-Gangetic Plains (IGPs).

The experiment was designed to evaluate the consequence of a blend of garlic oil and cinnamon bark powder administration on growth performance, nutrient digestibility, immunity, antioxidant status and methane emission in Murrah buffalo (Bubalus bubalis). Sixteen buffalo calves were divided into two groups in a completely randomised design. The first group (CONT) was fed a basal diet of wheat straw, green oats and concentrate mixture, whereas the second group (GOCB) received feeds as per the CONT along with a blend of garlic oil and cinnamon bark powder (0.5 mL + 1.0 g/head/day) by mixing it with the concentrate mixture for a period of 170 days. The growth rate and feed efficiency in GOCB group buffalo calves were improved (20%) with better (p < 0.05) digestibility of organic matter and crude proteins. Buffaloes of the GOCB group revealed enhanced (p < 0.05) immunity and antioxidant enzymes with reduced (p < 0.05) lipid peroxidation (26% less MDA production). The methane concentration in the eructed gas of the GOCB buffaloes was reduced (33.88%) in comparison with the CONT (p < 0.01). Thus, feed formulated with a blend of garlic oil-cinnamon bark powder demonstrates improvements in the health and production performances of buffalo calves.

The potential for plant-origin essential oils to modulate rumen functions for reducing bio-hydrogenation of fatty acids and methane production has been a significant area of research in recent times. This study investigated the effects supplementation of garlic (Allium sativum) essential oils have on in vitro bio-hydrogenation of fatty acids, methanogenesis and fermentation characteristics of total mixed ration in buffalo with the aim of enhancing conjugated linoleic acid (CLA) content in animal products as well as reducing environmental pollution. Allium sativum (AS) essential oils were examined at four levels [0 (Control), 33.33 µL (AS-1), 83.33 µL (AS-2) and 166.66 µL (AS-3) per litre of buffered rumen fluid] in a radio-frequency based automatic gas production system (ANKOM-RF). Two bottles per treatment per run over two incubation runs were undertaken to gain representative results. Oats hay and concentrate mixture (1:1) was used as a substrate (500 ± 5 mg) and incubated with 60 mL of buffered rumen fluid in 250 mL ANKOM bottles fitted with automatic an gas recording system at 39 °C for 24 h, following standard in vitro gas production protocols. The results demonstrated a reduction (p < 0.01) in lipid bio-hydrogenation, measured by lowered saturated fatty acids and enhanced unsaturated fatty acids on the supplementation of AS essential oils, irrespective of the dose levels. Moreover, the increased (p < 0.01) production of trans vaccenic (trans C18:1) acid (TVA) following graded dose supplementations of the AS essential oils increased the production of conjugated linoleic acids (CLA) in animal products. Although, reduced methane production (p < 0.01) was evidenced, the decrease in total gas production and feed digestibility (TDDM) demonstrated the strong antimicrobial properties of AS at all dose levels. The study reveals that the Allium sativam (Garlic) essential oils have the potential to be an agent for the reduction of the rumen biohydrogenation of fatty acids and methanogenesis. However, in vivo examination is necessary to validate the findings and confirm its suitability for use as an additive to enhance nutraceutical and organoleptic properties in animal products.

Appropriate irrigation scheduling, along with proper nutrient management practice for direct seeded rice (DSR), are very much essential to attain higher water use efficiency. Huge amounts of municipal waste are been produced every year and these wastes are left untreated and have caused many environmental hazards. However, these wastes can be converted into potential manures for crop production when enhanced with microbial consortia. Concerning these, the current research was carried out to know the effect of compost of enriched municipal soil waste (E-MSWC) with suitable irrigation scheduling on growth, yield, microbial activity, and water use efficiency of the DSR grown under Indo-Gangetic plains during two consecutive rice seasons of 2017–2018 and 2018–2019 at Varanasi, India. From the experiment, it was found that E-MSWC applied at 10 Mg·ha−1 along with 75% recommended dose of fertilizer (RDF) was capable to improve growth, yield, soil microbes, and water use efficiency (WUE) of rice. Amongst different enriched MSWC, the consortia (blend of N-fixing, P and Zn-solubilizing bacteria and Trichoderma) enriched MSWC was found to be the most effective. Concerning, different irrigation scheduling, it was observed that 50 mm cumulative pan evaporation (CPE) based irrigation was the most suitable as compared to providing irrigation at 75 mm CPE. Comparing rice varieties used in the research, the rice variety Swarna has appeared as a better choice in terms of yield and WUE than the variety, Sahbhagi. Thus, it can be recommended that irrigation at 50 mm of CPE in conjunction with 75% RDF + E-MSWC (consortia) at 10 Mg·ha−1 could improve the water use efficiency of rice grown in Indo-Gangetic plains.

Agriculture and forestry are the two major land use classes providing sustenance to the human population. With the pace of development, these two land use classes continue to change over time. Land use change is a dynamic process under the influence of multiple drivers including climate change. Therefore, tracing the trajectory of the changes is challenging. The artificial neural network (ANN) has successfully been applied for tracing such a dynamic process to capture nonlinear responses. We test the application of the multilayer perceptron neural network (MLP-NN) to project the future Agriculture, Forestry and Other Land Use (AFOLU) for the year 2050 for the South Asian Association for Regional Cooperation (SAARC) nations which is a geopolitical union of Afghanistan, Bangladesh, Bhutan, India, Nepal, Maldives, Pakistan and Sri Lanka. The Intergovernmental Panel on Climate Change (IPCC) and Food and Agriculture Organization (FAO) use much frequently the term ‘AFOLU’ in their policy documents. Hence, we restricted our land use classification scheme as AFOLU for assessing the influence of climate change scenarios of the IPCC fifth assessment report (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5). Agricultural land would increase in all the SAARC nations, with the highest increase in Pakistan and Maldives; moderate increase in Afghanistan, India and Nepal; and the least increase in Bangladesh, Bhutan and Sri Lanka. The forestry land use will witness a decreasing trend under all scenarios in all of the SAARC nations with varying levels of changes. The study is expected to assist planners and policymakers to develop nations’ specific strategy to proportionate land use classes to meet various needs on a sustainable basis.