Pollution indices of selected metals in tea (Camellia sinensis L.) growing soils of the Upper Assam region divulge a non-trifling menace of National Highway.

The study investigated the influence of a National Highway (NH) traversing tea estates (TEs) on heavy metal (HM) contamination in the top soils of Upper Assam, India. The dispersion and accumulation of six HMs, viz. cadmium (Cd), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), and zinc (Zn), within tea-growing soils were assessed using diverse indices: contamination factor (CF), degree of contamination (DC), enrichment factor (EF), geo-accumulation index (Igeo), modified degree of contamination (MDC), Nemerow pollution index (PINemerow), pollution load index (PLI), potential ecological risk factor (Eri), and potential ecological risk index (RI). The order of HM prevalence was Fe > Mn > Zn > Ni > Cu > Cd. Elevated Cd levels near the NH prompted immediate attention, while Cd and Zn showed moderate pollution in CF, EF, and RI. The remaining metals posed minimal individual risk (Eri< 40), resulting in an overall contamination range of "nil to shallow," signifying slight contamination from the studied metals. From MDC values for investigated metals, it was found to be "zero to very low degree of contamination" at all locations except the vicinity of NH. Soil pollution, as determined by PLI, indicated unpolluted soils in both districts, yet PINemerow values indicated slight pollution. The statistical analysis revealed that there is a significant decrease in most of the indices of HM as the distance from NH increases. The application of multivariate statistical techniques namely Principal Component Analysis and Cluster Analysis showed the presence of three distinct homogenous groups of distances based on different indices. This investigation underscores NH-associated anthropogenic effects on TE soil quality due to HM deposition, warranting proactive mitigation measures.

MethSemble-6mA: an ensemble-based 6mA prediction server and its application on promoter region of LBD gene family in Poaceae.

The Lateral Organ Boundaries Domain (LBD) containing genes are a set of plant-specific transcription factors and are crucial for controlling both organ development and defense mechanisms as well as anthocyanin synthesis and nitrogen metabolism. It is imperative to understand how methylation regulates gene expression, through predicting methylation sites of their promoters particularly in major crop species. In this study, we developed a user-friendly prediction server for accurate prediction of 6mA sites by incorporating a robust feature set, viz., Binary Encoding of Mono-nucleotide DNA. Our model,MethSemble-6mA, outperformed other state-of-the-art tools in terms of accuracy (93.12%). Furthermore, we investigated the pattern of probable 6mA sites at the upstream promoter regions of the LBD-containing genes in Triticum aestivum and its allied species using the developed tool. On average, each selected species had four 6mA sites, and it was found that with speciation and due course of evolution in wheat, the frequency of methylation have reduced, and a few sites remain conserved. This obviously cues gene birth and gene expression alteration through methylation over time in a species and reflects functional conservation throughout evolution. Since DNA methylation is a vital event in almost all plant developmental processes (e.g., genomic imprinting and gametogenesis) along with other life processes, our findings on epigenetic regulation of LBD-containing genes have dynamic implications in basic and applied research. Additionally, MethSemble-6mA (http://cabgrid.res.in:5799/) will serve as a useful resource for a plant breeders who are interested to pursue epigenetic-based crop improvement research.

The Level of Selected Metals in Made Tea and Tea Infusion from the Roadside Tea Plants and Health Risk Assessment.

The effects of human activities are becoming clearer every year, with multiple reports of struggling and eroded ecosystems resulting in new threats of plant and animal extinctions throughout the world. It has been speculated that roadside tea-growing soils impact on metal dynamics from soil to tea plants and subsequently to tea infusion which may be threatened by increasingly unpredictable and dangerous surroundings. Furthermore, heavy metals released from vehicles on the national highway (NH) could be a source of metal contamination in roadside tea soils and tea plants. This study was articulated to realize the effect of NH on a buildup of selected metals (Cu, Cd, Fe, Mn, Ni, and Zn) in made tea along with repeated tea infusion. In general, metal concentration was found significantly higher in made tea prepared from the young shoots collected from the vicinity of NH. The results also showed that distance from the NH and infusion process significantly influenced to content of the analysed metal in tea infusions. The mean average daily intake (ADI) and hazard quotient (HQ) values of analysed tea samples were found in the orderMn˃Fe˃Zn˃Cu˃Ni˃Cd and Mn˃Cu˃Zn˃Fe˃Ni˃Cd, respectively. The HQ values of all analysed metals were found << 1, indicating that ingestion of tea infusion with analysed heavy metals should not cause a danger to human health. However, this study further demonstrates the consumption of tea infusion prepared from made tea around the vicinity of NH may contribute to a significantly higher quantity of metal intake in the human body. From the hierarchical cluster analysis, it has been observed that there are three homogenous groups of analysed heavy metals.

Change in the air due to the coronavirus outbreak in four major cities of India: What do the statistics say?

The onset of the novel Coronavirus (COVID-19) has impacted all sectors of society. To avoid the rapid spread of this virus, the Government of India imposed a nationwide lockdown in four phases. Lockdown, due to COVID-19 pandemic, resulted a decline in pollution in India in general and in dense cities in particular. Data on key air quality indicators were collected, imputed, and compiled for the period 1st August 2018 to 31st May 2020 for India's four megacities, namely Delhi, Mumbai, Kolkata, and Hyderabad. Autoregressive integrated moving average (ARIMA) model and machine learning technique e.g. Artificial Neural Network (ANN) with the inclusion of lockdown dummy in both the models have been applied to examine the impact of anthropogenic activity on air quality parameters. The number of indicators having significant lockdown dummy are six (PM2.5, PM10, NOx, CO, benzene, and AQI), five (PM2.5, PM10, NOx, SO2 and benzene), five (PM10, NOx, CO, benzene and AQI) and three (PM2.5, PM10, and AQI) for Delhi, Kolkata, Mumbai and Hyderabad respectively. It was also observed that the prediction accuracy significantly improved when a lockdown dummy was incorporated. The highest reduction in Mean Absolute Percentage Error (MAPE) is found for CO in Hyderabad (28.98%) followed by the NOx in Delhi (28.55%). Overall, it can be concluded that there is a significant decline in the value of air quality parameters in the lockdown period as compared to the same time phase in the previous year. Insights from the COVID-19 pandemic will help to achieve significant improvement in ambient air quality while keeping economic growth in mind.

Climate plays a dominant role over land management in governing soil carbon dynamics in North Western Himalayas.

The soil carbon (C) dynamics is strongly influenced by climate and land-use patterns in the Himalayas. Therefore, soils under five prominent land use [e.g., maize (Zea mays), horticulture, natural forest, grassland, and wasteland] were sampled down up to 30 cm depth under two climatic conditions viz., temperate and subtropical to assess the impacts of climate and landuse on soil C dynamics. Results demonstrated that irrespective of land use, temperate soil contains 30.66% higher C than subtropical soils. Temperate soils under natural forests had the higher total organic carbon (TOC, 21.90 g kg-1), Walkley-Black carbon (WBC, 16.42 g kg-1), contents, and stocks (TOC, 66.92 Mg ha-1 and WBC, 50.24 Mg ha-1), and total soil organic matter (TSOM, 3.78%) concentration as compared to other land uses like maize, horticulture, grassland, and wasteland. Under both climatic conditions, maize land use had the lowest TOC 9.63, 6.55 g kg-1 and WBC 7.22, 4.91 g kg-1 at 0-15 and 15-30 cm soil depth, respectively. Horticulture land use had 62.58 and 62.61% higher TOC and WBC over maize-based land use under subtropical and temperate climatic conditions at 0-30 cm soil depth, respectively. However, soils of maize land use under temperate conditions had ∼2 times more TOC than in subtropical conditions. The study inferred that the C-losses is more in the subtropical soil than in temperate soils. Hence, the subtropical region needs more rigorous adoption of C conservation farming practices than the temperate climatic setting. Although, the adoption of C storing and conserving practices is crucial under both climatic settings to arrest land degradation. Horticultural land uses along with conservation effective soil management practices may be encouraged to restore more soil C and to improve the livelihood security of the hill populace in the North Western Himalayas.

Estimation of Heritability under Correlated Errors Using the Full-Sib Model.

In plant and animal breeding, sometimes observations are not independently distributed. There may exist a correlated relationship between the observations. In the presence of highly correlated observations, the classical premise of independence between observations is violated. Plant and animal breeders are particularly interested to study the genetic components for different important traits. In general, for estimating heritability, a random component in the model must adhere to specific assumptions, such as random components, including errors, having a normal distribution, and being identically independently distributed. However, in many real-world situations, all of the assumptions are not fulfilled. In this study, correlated error structures are considered errors that are associated to estimate heritability for the full-sib model. The number of immediately preceding observations in an autoregressive series that are used to predict the value at the current observation is defined as the order of the autoregressive models. First-order and second-order autoregressive models i.e., AR(1) and AR(2) error structures, have been considered. In the case of the full-sib model, theoretical derivation of Expected Mean sum square (EMS) considering AR(1) structure has been obtained. A numerical explanation is provided for the derived EMS considering AR(1) structure. The predicted mean squares error (MSE) is obtained after including the AR(1) error structures in the model, and heritability is estimated using the resulting equations. It is noticed that correlated errors have a major influence on heritability estimation. Different correlation patterns, such as AR(1) and AR(2), can be inferred to change heritability estimates and MSE values. To attain better results, several combinations are offered for various scenarios.

Application of analytical hierarchical process, multi-influencing factor, and geospatial techniques for groundwater potential zonation in a semi-arid region of western India.

Groundwater resources are alarmingly depleting due to over-exploitation and significant climate changes over time. Therefore, demarcation of groundwater potential zones is essential for addressing the needs of various industries in semi-arid area. Depleting groundwater resources, topography, aquifer features and climatic factors make it necessary to demarcate ground water potential zones in semiarid region of Rajasthan. The Analytical Hierarchy Process (AHP), Geographic Information System (GIS), and Multi Influence Factor (MIF) were used to determine the groundwater potential zones (GWPZs) in the semi-arid region of Jaipur, located in western Rajasthan. In present study, ten influential factors were employed i.e., geomorphology, land use/land cover (LULC), drainage density, rainfall, topographic wetness index (TWI), soil texture, slope, roughness, topographic position index (TPI) and curvature. In AHP technique, the pairwise comparison matrix was generated, and weightages were given to each thematic layer while for MIF, a proposed score for each layer was computed from the aggregate weight of major and minor effects. The GWPZ map generated by AHP technique was categorised into three parts: high, moderate and poor potential zones, covering 13%, 50.7% and 36.3% of the district. While, the GWPZ map produced with the MIF technique was also divided into the same poor, moderate, and high categories, encompassing 35.3, 44.1, and 20.6% of the district, respectively. The results of AHP and MIF techniques were then cross-validated with well depth data obtained from CGWB report, 2019-20. The receiver operating characteristics (ROC) were plotted and the findings shows that the Area under the Curve (AUC) was 79% and 76% for AHP and MIF, respectively which is considered as moderate to high in predictive precision. The study would be helpful in locating drilling sites for groundwater exploration and developing sustainable groundwater and land use policies.

Insights into the effects of tea pruning litter biochar on major micronutrients (Cu, Mn, and Zn) pathway from soil to tea plant: An environmental armour.

A field study was conducted from 0 to 360 days to investigate the effect of tea pruning litter biochar (TPLBC) on the accumulation of major micronutrients (copper: Cu, manganese: Mn, and zinc: Zn) in soil, their uptake by tea plant (clone: S.3 A/3) and level of contamination in soil due to TPLBC. To evaluate the level of contamination due to TPLBC, a soil pollution assessment was carried out using the geo-accumulation index (Igeo), enrichment factor (EF), contamination factor (CF), potential ecological risk factor (PERF), individual contamination factor (ICF), and risk assessment code (RAC). The total content of Cu, Mn, and Zn gradually increased with increasing doses of TPLBC at 0D, and then decreased with time. The fractionation of the three micronutrients in soil changed after the application of TPLBC. The contamination risk assessment of soil for Cu, Mn, and Zn based on the Igeo, EF, CF, PERF,ICF, and RAC suggested that the application of TPLBC does not have any adverse effect on soil. Except for Mn, the bioconcentration and translocation factors were less than one for Cu and Zn. Results from this study revealed that the application of 400 kg TPLBC ha-1 is significantly better than the other treatments for Cu, Mn, and Zn at a 5% level of significance.

Land-use systems regulate carbon geochemistry in the temperate Himalayas, India.

The Himalayan ecosystem is critical for ecological security and environmental sustainability. However, continuous deforestation is posing a serious threat to Himalayan sustainability. Changing land-use systems exert a tenacious impact on soil carbon (C) dynamics and regulate C emissions from Himalayan ecosystem. Therefore, this study was conducted to determine the changes in different C pools and associated soil properties under diverse land-use systems, viz. natural forest, natural grassland, maize field converted from the forest, plantation, and paddy field of temperate Himalaya in the surface (0-20 cm) and subsurface (20-40 cm) soils. The highest total organic carbon (24.24 g kg-1) and Walkley-black carbon contents (18.23 g kg-1), total organic carbon (45.88 Mg ha-1), and Walkley-black carbon stocks (34.50 Mg ha-1) were recorded in natural forest in surface soil (0-20 cm depth), while soil under paddy field had least total organic carbon (36.45 Mg ha-1) and Walkley-black carbon stocks (27.40 Mg ha-1) in surface soil (0-20 cm depth). The conversion of natural forest into paddy land results in 47.36% C losses. Among the cultivated land-use system, minimum C losses (29.0%) from different pools over natural forest system were reported under maize-filed converted from forest system. Land conversion causes more C losses (21.0%) in surface soil (0-20 cm depth) as compared to subsurface soil. Furthermore, conversion of forest land into paddy fields increased soil pH by 5.9% and reduced total nitrogen contents and microbial population by 28.0% and 7.0%, respectively. However, the intensity of total nitrogen and microbial population reduction was the lowest under maize fields converted from the forest system. The study suggested that the conversion of natural forest to agricultural land must be discouraged in the temperate Himalayan region. However, to feed the growing population, converted forest land can be brought under conservation effective maize-based systems to reduce C loss from the intensive land use and contribute to soil quality improvements and climate change mitigation.

COVID-19 and prices of pulses in Major markets of India: Impact of nationwide lockdown.

The COVID-19 pandemic has impacted almost all the sectors including agriculture in the country. The present paper investigates the impact of COVID-19 induced lockdown on both wholesale and retail prices of major pulses in India. The daily wholesale and retail price data on five major pulses namely Lentil, Moong, Arhar, Urad and Gram are collected for five major markets in India namely Delhi, Mumbai, Kolkata, Chennai and Hyderabad during the period January, 2019 to September, 2020 from Ministry of Consumer Affairs, Food & Public Distribution, Government of India. The Government of India declared nationwide lockdown since March, 24, to May, 31, 2020 in different phases in order to restrict the spread of the infection due to COVID-19. To see the impact of lockdown on price and price volatility, time series model namely Autoregressive integrated moving average (ARIMA) model with error following Generalized autoregressive conditional heteroscedastic (GARCH) model incorporating exogenous variable as lockdown dummy in both mean as well variance equations. It is observed that in almost all the markets, lockdown has significant impact on price of the pulses whereas in few cases, it has significant impact on price volatility.

Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India.

BACKGROUND: Price forecasting of perishable crop like vegetables has importance implications to the farmers, traders as well as consumers. Timely and accurate forecast of the price helps the farmers switch between the alternative nearby markets to sale their produce and getting good prices. The farmers can use the information to make choices around the timing of marketing. For forecasting price of agricultural commodities, several statistical models have been applied in past but those models have their own limitations in terms of assumptions. METHODS: In recent times, Machine Learning (ML) techniques have been much successful in modeling time series data. Though, numerous empirical studies have shown that ML approaches outperform time series models in forecasting time series, but their application in forecasting vegetables prices in India is scared. In the present investigation, an attempt has been made to explore efficient ML algorithms e.g. Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machine (GBM) for forecasting wholesale price of Brinjal in seventeen major markets of Odisha, India. RESULTS: An empirical comparison of the predictive accuracies of different models with that of the usual stochastic model i.e. Autoregressive integrated moving average (ARIMA) model is carried out and it is observed that ML techniques particularly GRNN performs better in most of the cases. The superiority of the models is established by means of Model Confidence Set (MCS), and other accuracy measures such as Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Prediction Error (MAPE). To this end, Diebold-Mariano test is performed to test for the significant differences in predictive accuracy of different models. CONCLUSIONS: Among the machine learning techniques, GRNN performs better in all the seventeen markets as compared to other techniques. RF performs at par with GRNN in four markets. The accuracies of other techniques such as SVR, GBM and ARIMA are not up to the mark.

Bioefficacy evaluation of ferrocenyl chalcones against Meloidogyne incognita and Sclerotium rolfsii infestation in tomato.

The present study reports, bioefficacy evaluation of effective compounds against Meloidogyne incognita and Sclerotium rolfsii in pot cultured tomato. The identified five most effective compounds, i.e. (2E)-1-(4-Methylphenyl)-3-ferrocenyl-prop-2-en-1-one (6g), (2E)-1-(4-Methoxyphenyl)-3-ferrocenyl-prop-2-en-1-one (6h), (2E)-1-(3-Bromophenyl)-3-ferrocenyl-prop-2-en-1-one (6j), (2E)-1-(2,4-Dichlorophenyl)-3-ferrocenyl-prop-2-en-1-one (6k) and (2E)-1-(3,5-Dichloro-2-hydroxyphenyl)-3-ferrocenyl-prop-2-en-1-one (6p) along with Carbofuran 3G as positive control were tested at 20, 40 and 80 ppm by soil drenching and root dipping methods. The study revealed that all plant growth parameters were positively influenced by these compounds. The presence of an electron releasing group positively influenced the efficacy, and the activity was highest in compounds 6g and 6h at 80 ppm. Based on in vitro results against S. rolfsii, (2E)-1-Ferrocenyl-3-(4-bromophenyl)-prop-2-en-1-one (3b), (2E)-1-Ferrocenyl-3-(2,6-dichlorophenyl)-prop-2-en-1-one (3o) and (2E)-1-(5-Chloro-2-hydroxyphenyl)-3-ferrocenyl-prop-2-en-1-one (6o) along with Tebuconazole 25.9% EC and Hexaconazole 5% SC as positive control were evaluated. The shoot length was found to be highest (24.50 cm) in plants treated with 3b followed by 3o and 6o at 1000 ppm. The percent disease incidence was significantly decreased as compared to control. The percent disease incidence was found to be minimum in plants treated with 3b at 1000 ppm. However, root dipping was not as effective as soil drenching. Therefore, ferrocenyl chalcone derivatives proved to be of great fungicidal and nematicidal potential opening new opportunities for expanding their effectiveness as new pest control agents.

Sick or rich: Assessing the selected soil properties and fertility status across the tea-growing region of Dooars, West Bengal, India.

Harnessing the potential yields of evergreen perennial crops like tea (Camellia sinensis L.) essentially requires the application of optimum doses of nutrients based on the soil test reports. In the present study, the soil pH, organic carbon (OC), available potassium as K2O (AK), and available sulphur (AS) of 7300 soil samples from 115 tea estates spread over the Dooars ranging from 88°52'E to 89°86'E longitude and 26°45'N to 27°00'N latitude of West Bengal, India have been documented. About 54% of soil samples were found within the optimum range of soil pH (4.50-5.50) for tea cultivation. The overall range of OC was found from 0.28% to 6.00% of which, 94% of the analyzed samples were within the range of satisfactory to excellent level of OC i.e. (>0.80% to 6.00%). Around 36.3% of soil samples were found to have high AK (>100 mg kg-1) but 37.1% of soils were found to have high AS content (>40 mg kg-1). The nutrient index status of soil pH was low in Dam Dim, Chulsa, Nagrakata, Binnaguri, and Jainti sub-districts. Soils from five sub-districts had a high nutrient index (2.47 to 2.83) for soil organic carbon. However, it existed in the medium index (1.69 and 2.22) for Dalgaon and Kalchini sub-districts. Only Nagrakata sub-district soil samples were in the high nutrient index (2.65) for AK. All analyzed samples showed a medium nutrient index (1.97 to 2.27) for AS. The result indicated that soil pH was significantly negatively correlated with soil OC (-0.336) and AK (-0.174). However, the soil OC was significantly positive correlated with AK (0.258) and AS (0.100). It could be concluded that a balanced fertilizer application would be needed as a part of the soil improvement program through soil chemical tests for sustainable tea cultivation.