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The sole purpose of this review paper is to reveal the land use and land cover (LULC) changes in the study region (Northern India) from 2017 to 2021. The researchers discovered that no systematic review of the LULC dynamics of the study region had been conducted so far. This study used a novel systematic review procedure (i.e., the ICA framework) to conduct the review process. The data for this study were retrieved from the Web of Science of Clarivate Analytics, using keywords related to LULC. A thorough review of 29 full-text articles was done to examine the primary land use classes (LUC), techniques used, factors responsible, and dynamics of the major land classes. Most articles included agriculture, built-up, water bodies, and plantations as the main LULC categories. Most of them used on-screen digitisation and supervised algorithms to classify LULC classes and incorporated accuracy assessment to validate their results. It was also found that almost all the papers cited climate change, increasing population, urbanisation, and economic considerations as the main drivers responsible for the changes that occurred in different LULCs. The findings of the various studies revealed that horticulture and built-up areas have increased while agricultural, forest cover and open spaces have decreased to a greater extent. Environmental negatives of LULC dynamics of the region suggested that LULC change is not favourable everywhere. Thus, to control the haphazard LULC change, the government must come up with a substantial land conversion policy in the region with ground-level implantation that will restrict the people from blind land conversion. [ABSTRACT FROM AUTHOR]

A comparative study of termite diversity in different ecosystems (agricultural, horticultural and silvicultural ecosystems) was carried out in mid-hills of Meghalaya to understand the species diversity, richness and abundanceof termite. A standard transect method and tissue paper bait method were used for sampling the termites.Alpha diversity was analysed by using Shannon wiener index and Margalef's D index for Species richness; and Simpson's index for Species dominance; whereas Beta diversity was analysed by using Routledge Be index, Cody Bc index, Whittaker Bw index and Wilson and Shmida Bt index. In the study, a total of 10 species of termites were identified which belonged to three subfamilies viz., Macrotermitinae, Amitermitinae and Termitinae. Macrotermitinae was the most presiding subfamily among the collected termite fauna. Individual species dominance was observed to differ in different ecosystems however, Odontotermes spp. was widely distributed across the ecosystems.The number of species recorded in silvicultural, horticultural and agricultural ecosystem were 7, 3 and 1 respectively. The termite diversity was highest in silvicultural ecosystem and lowest in agricultural ecosystem. The lowest numbers of species recorded in ecosystem may be attributed to its abrupt human disturbance. Shannon Wiener, Margalef's D and Simpson index in agricultural ecosystem, depicted less values compared to horticultural and silvicultural ecosystem. Among the ten species collected, one species i.e. Pseudocapritermis tikaderi which was sampled from silvicultural ecosystems was soil/humus feeder and the rest were wood/litter feeders. [ABSTRACT FROM AUTHOR]

Background and Aims: The accessibility and relatively low cost of nitrogen (N) fertilizers have been a gift to humankind and made it possible to feed the exponentially growing world population. The excessive use of N, in combination with a poor N use efficiency (NUE) in crop production, is associated with severe environmental impacts, and the scientific community has repeatedly warned that the safe operating space for N within the planetary boundaries has been exceeded. In the light of these considerations, a global player and India's largest fertilizer manufacturer, the Indian Farmers Fertilizer Cooperative (IFFCO), has recently developed and patented a nanotechnology-based fertilizer marketed as Nano Urea (liquid). Strikingly, the producers state that it is now possible to replace a 45 kg bag of conventional urea, containing 21 kg N, by foliar application of just 20 g N in the form of nano urea (NU). If so, N from this novel high-tech fertilizer product should be able to increase the NUE of crops more than 1000 times compared to conventional urea. This year, the Indian government and IFFCO announced plans to massively boost production by building 10 new factories, with an annual production capacity of 440 million bottles NU by 2025 and to expand export of the product to another 25 countries, mainly situated in Asia, Africa and South-America. In the marketing of NU, IFFCO states that there is scientific evidence for distinct beneficial properties in terms of higher crop yields and reduced negative environmental impacts. Similar ambitions have recently been presented for Nano Zinc, Nano Copper, and Nano Di-Ammonium-Phosphate, a product that entered the market in March 2023. Methods: In this Opinion Paper, we compare the claims made by IFFCO scientists to the existing scientific evidence in the field of foliar nanofertilizers. Results: We observe that NU is a poorly described product with no scientifically proven effects. The product is marketed with misleading and wrong statements about its fertilizer efficiency, the underlying plant uptake pathways, and the environmental friendliness. Conclusion: The expectations raised by IFFCO are far from reality and may lead to large-scale yield losses with serious consequences for food security and the livelihood of farmers. At the same time, the confidence in innovative sustainable products as well as the science behind them may be threatened. Based on the IFFCO case, and considering the booming emergence of novel nano based fertilizers appearing all over the world these years, it is clear that much more priority should be given to scientifically prove their efficacy and mode of action, before they are launched on the markets. [ABSTRACT FROM AUTHOR]

The rampant adoption of digital technologies made momentous changes in all economic sectors. The agriculture sector cannot abstain from the digital revolution. Agriculture and farming are one of the oldest and most important professions in India. The sector remains the backbone of the Indian rural economy, which desperately demands technological impetus for the socio-economic development of rural areas. Smart agriculture is a revolution in the agriculture industry which helps to guide the actions that are required to modify and reorient the agricultural systems. The paper made an extensive survey of various technologies proposed for the agriculture sector. We have surveyed various smart agricultural applications developed and proposed a taxonomy for classifying them. Network infrastructure and connectivity remains the major challenge for rural areas. The paper explores the viability of deploying IoT-based technologies in agricultural sectors along machine learning techniques to optimize resource utilization, planning and cultivation, marketing, pesticide selection, price prediction, etc. An in-depth coverage of recent research works is also mentioned which will help the future researchers to address specific challenge and adopt suitable technology to help the farmers to improve their productivity and better decision making in cultivation. Apart from listing the applications, we also propose an architecture for smart agriculture and implemented smart price prediction model for crops like cotton and cardamom along with a prototype for smart irrigation. [ABSTRACT FROM AUTHOR]

The state of India was situated on fertile land and river deltas with appropriate agricultural land. In 2019, agricultural fields, primarily cropland, occupy more than 40% of the country's total surface area. Moreover, agricultural industry revenues less than 3% of the country's Gross Provincial Product (GPP). While manufacturing has become the country's major financial activity, accounting countries represent half of GPP's revenues. The objective of the study is to find a way to improve the financial profitability and efficiency of farming supply chain networks as follows: (1) Fixation of national-level targets for zonal-level groups information after assessment and forecasts that affect production and distribution of agriculture. (2) Producers risk can be reduced by directing people to multiple factory and industrialization options based on market assessment. (3) Insurance costs and reduction of bank borrowing by standardizing the connection between bankers and producers using the structure to centralise land information. (4), Stabilize the agricultural sector by looking at nearby potential destinations for production and regulation of the Public Distribution System (PDS) flow for the security stock. In this paper, the novel ML target prediction algorithm to inform the farmers about the market target product and improve the relationships between the farmer and bankers for centralizing the information about recent government plans. The crop prediction ML algorithm proposed to improve the revenue of agriculture field. [ABSTRACT FROM AUTHOR]

The future of agriculture may be fully realized using knowledge of artificial intelligence (AI) accumulated by human expertise. With increasing climate change and population pressure challenges, new technologies, such as AI and RS in vital sectors like agriculture is urgently required to assess agricultural suitability, optimize productivity, and then support sustainable development. This paper presents bibliometric and review analyses on AI and geospatial analysis applied to the agricultural field. Based on the prestigious database, Scopus, thousands of documents were retrieved in the period 1992–2021 and processed using Scopus online tool and VOSviewer software. The results show a growing trend in the number of publications during this period. Earth and Planetary Sciences is the top domain, remote sensing (RS) is the leading source, while the United States and India are the most influential countries. RS, machine learning (ML), GIS, land-use, decision-making, decision trees, and satellite imagery are the most occurring topics in this search. Otherwise, research in this field is turning toward ML and deep learning techniques, whereas agricultural robots and antennas are the top trending tools. The current study provides specific information about the connections between artificial intelligence, geospatial analysis, and agriculture in the period 1992–2021, the fruitful period where the AI coupled to geospatial technologies is in full development. [ABSTRACT FROM AUTHOR]

The paper discusses the work of early Indian professional economists in the field of labour. They undertook wide-ranging studies of labour in factories, mines, and plantations as well as of the condition of rural and agricultural labour and of urban labour outside of industry. Between 1918 and 1930, a major body of work had emerged on Indian labour, with contributions by both Indian and foreign scholars. This work was mostly based on government data, but, in addition, and with telling effect, some scholars also conducted field investigations and came up with disquieting findings. The latter part of the paper examines the contributions of a British statistician-economist, George Findlay-Shirras, and of an Indian labour economist, Rajani Kanta Das. The former, despite some excellent work, was probably responsible for giving a misleading picture of rising agricultural wages, perhaps to suit the views of the colonial government. R.K. Das, through his extensive work on labour economics, before and during his service with the International Labour Organization (ILO), contributed much to the work of the Royal Commission on Labour (1931), but this went unacknowledged. Also, his work on plantation labour in India earned the ire of a British official in the department of industries and labour probably, for highlighting the negative role played by foreign (British) capital. Given his numerous substantive contributions to Indian labour economics, the paper concludes that R.K. Das should be given due recognition for his pioneering role in the development of labour economics in India. [ABSTRACT FROM AUTHOR]

The fund allocation in agricultural sector in India is heavily tilted toward input subsidies provision; however, researchers seem to favor investment expenditure instead. The present paper seeks to compare the usefulness of input subsidies as compared to investment with regard to agricultural productivity so that policy makers hit the right tool and avoid less productive state expenditure. We investigated a total of four regression models using autoregressive and distributed lag cointegration in a time series framework covering period from 1983 to 2019. The first model considers all input subsidies in aggregate form, and the rest three models take input subsidies in disaggregate forms, namely fertilizer subsidy, irrigation subsidy and power subsidy, respectively. It is observed from the results that input subsidies still contribute more than what public investment does to agricultural productivity. It is also found that power subsidy is the most effective component of input subsidies followed by fertilizer subsidy. Hence, government expenditure on input subsidies is justified on the ground that it ensures all farmers to have access to affordable agricultural inputs. Targeted subsidies combined with adequate investment in agricultural infrastructure could deliver long-term agricultural development in India. [ABSTRACT FROM AUTHOR]

Cadmium pollution turns out to be a global environmental problem. This study conducted a quantitative and qualitative bibliometric analysis based on 9188 research items from the Web of Science Core Collection published in the last 20 years (2000–2020), presenting an in-depth statistical investigation of global freshwater cadmium research progress and developing trend. Our results demonstrated that the researchers from China, the USA, and India contribute the most to this field. The primary sources of cadmium are mining, industry, wastewater, sedimentation, and agricultural activities. In developing countries, cadmium exposure occurs mainly through the air, freshwater, and food. Fish and vegetables are the main food sources of cadmium for humans because of their high accumulation capability. Source evaluation, detection, and remediation represent the main technologies used to clean up cadmium-contaminated sites. To mitigate the risk of cadmium contamination in freshwater, biomarker-based cadmium monitoring methods and integrated policies/strategies to reduce cadmium exposure merit further concern. [ABSTRACT FROM AUTHOR]

Digitalization accumulates data in a short period. Smart agriculture for crop identification for cultivation is a common problem in agriculture for agronomists. The generated data due to digitalization does not provide any useful information unless some meaningful information is retrieved from it. Therefore from the existing information system, prediction of decision for unseen associations of attribute values is of challenging. This paper presents a model that hybridizes a fuzzy rough set, real-coded genetic algorithm, and linear regression. The model works in two phases. In the initial phase, the fuzzy rough set is used to remove superfluous attributes whereas, in the second phase, a real-coded genetic algorithm is used to predict the decision values of unseen instances by making use of linear regression. The proposed model is analyzed for its viability using agricultural information system obtained from Krishi Vigyan Kendra of Thiruvannamalai district of Tamilnadu, India. Further, the accuracy of the proposed model is compared with existing techniques. [ABSTRACT FROM AUTHOR]

Farmers submit claims to insurance providers when affected by sowing/planting risk, standing crop risk, post-harvest risk, and localized calamities risk. Decision making for settlement of claims submitted by farmers has been observed to comprise of type-I and type-II errors. The existence of these errors reduces confidence on agri-insurance providers and government in general as it fails to serve the needy farmers (type-I error) and sometimes serve the ineligible farmers (type-II error). The gaps in currently used underlying data, methods and timelines including anomalies in locational data used in crop sampling, inclusion of invalid data points in computation, estimation of crop yield, and determination of the total sown area create barriers in executing the indemnity payments for small and marginal farmers in India. In this paper, we present a satellite big data analytics based case study in a region in India and explain how the anomalies in the legacy processes were addressed to minimize type-I and type-II errors and thereby make ethical decisions while approving farmer claims. Our study demonstrates what big data analytics can offer to increase the ethicality of the decisions and the confidence at which the decision is made, especially when the beneficiaries of the decision are poor and powerless. [ABSTRACT FROM AUTHOR]

This paper aims at synthesizing the scientific research on coal fly ash and agriculture using bibliometric analysis. The research on fly ash and agriculture has grown at 6.7% annually during 1973–2022, where much attention has been received after 2007. The number of publications on coal fly ash and agriculture reached above 100 in the year 2010, which further increased to 299 by 2020. The research productivity has been assessed as the most influential countries, journals, and documents in terms of a number of publications and citations on fly ash and agriculture. India is the most productive country in terms of knowledge on coal fly ash and agriculture with 38% of the global publications, while Environmental Science and Pollution Research is the leading journal with 19 publications. The article on assessing the impact of fly ash incorporation in soil systems by Pandey and Singh (2010), having 349 citations with 26.85 citations per year is one of the leading publications globally. Further, science mapping has been conducted to comprehend the current research focus and discover the emerging themes for future research. The finding of the thematic map based on the level of development and importance indicate that FA can be effectively explored as soil-additive for improved physical, chemical and biological properties and enhanced plant nutrients. The findings provide several aspects of fly ash and suggest future research directions to study the potential of using coal fly ash in agriculture to gain an agronomic advantage. [ABSTRACT FROM AUTHOR]

Nutrition-sensitive agriculture (NSA) has emerged as a major development paradigm that works to diversify crops and diets throughout the Global South in order to improve nutritional outcomes. Drawing on a conceptual framework from political ecologies of health that looks at political economic factors, social discourse, and embodied, material experiences of food, I analyze qualitative and ethnographic data from an integrated NSA intervention in Madhya Pradesh and Jharkhand, India. The analysis shows that while embodied experiences of differing rice varieties (either indigenous or improved) were central to research participants' conceptions of bodily health, mainstream NSA metrics had trouble 'seeing' these relations in meaningful ways. Moreover, although material experiences of rice cultivation and consumption anchored participants' rice preferences, structural economic realities along with notions of social identity were always interwoven. Yet, while villagers expressed divergent perceptions around how the different rice cultivars shaped their bodily health, agricultural officers tended to view rice experiences as a product of culture, rather than as material and socio-ecological food-body interactions. In sum, this paper argues more deeply engaging with the political economic, socially symbolic, and embodied ways that communities relate to food production and consumption would allow NSA research to develop more grounded and inclusive understandings of agriculture-nutrition linkages. [ABSTRACT FROM AUTHOR]

The paper contributes to the growing literature highlighting the significance of assessing risk and vulnerability, micro-level perceptions and adaptation decision-making in building resilience of farm communities to climate change in dryland region of India. To select a region for grassroots enquiry, spatial differential in risk to climate change was assessed in Rajasthan, using IPCC AR5 framework. Among the highly vulnerable and risky districts, Bikaner district was selected for elicitation of micro-level imperatives. Rising atmospheric temperature, inter-seasonal displacements of rainfall and recurrence of extreme events were perceived by the farmers resulting in resource degradation, production risks and erosion of households' socio-economic dynamics. As risk preventive measures, suitable adjustment in agricultural practices, natural resource management, shift to off-farm activities and other relief measures were adopted by the farmers. Farmer's choice of adaptation was influenced by several climatic, socio-economic and infrastructural & institutional factors in varying degree. Moreover, several financial, economic, infrastructural and informational bottlenecks to adaptations were reported during household survey and FGDs. The results suggest that capturing grassroots evidence is crucial for directing locally tailored adaptation strategies, along with the improving deficiencies in the developmental pathways for climate-resilient agriculture. [ABSTRACT FROM AUTHOR]

Groundwater availability in upland villages in southern India has reduced drastically during the last two decades. Most farmers believe this is entirely due to the decrease in rainfall. The current work presents participatory system dynamics modeling of crop-water-income dynamics and root cause analysis in one such village to quantify the long-term effects of groundwater withdrawal and other agricultural practices. Groundwater overexploitation was identified as the most significant cause of the crisis. Causal loop diagrams, stocks and flows diagrams, and scenario analysis were developed through focused group discussions and individual meetings with stakeholders. Multiple 'what-if' scenarios suggested by stakeholders were simulated and analyzed. Notably, less water-intensive, chemical-free farming emerged as an ecologically and economically sustainable strategy. A post-project survey indicated that 90% of stakeholders felt that the framework enhanced their understanding of the problem, encouraged unbiased exploration of new ideas, and raised their commitment to subsequent action plans. It is an easily replicable framework for adoption by farmers and policymakers to address the groundwater crisis and promote sustainable farming practices. [ABSTRACT FROM AUTHOR]

Globally, agriculture is recognized as a highly vulnerable sector to climate change and risks from climatic aberrations pose an imminent danger to the food security and sustainability of livelihoods. To bring robustness in climate adaptation planning, evaluation of resilience across homogenous regions is essential for developing and scaling suitable location-need-context specific interventions and policies that build the resilience of the agricultural system. In this paper, we present an analysis and discussion of multi-scalar and multi-indicator assessment, by profiling resilience across agro-climatic zones of India, based on the development of a Climate-Resilient Agriculture Index embracing environmental, technological, socio-economic, and institutional and infrastructural dimension. A total of 26 indicators, spread across these four dimensions, were employed to purport inter- and intra-agro-climatic zone differentials in the level of resilience. Among the zones, it was found that West Coast Plains & Ghats and Tans-Gangetic Plains had the highest degree of resilience to manage climate risks. Most of the districts lying within Eastern Himalayan Region, Middle Gangetic Plains, Eastern Plateau & Hills, and Western Dry Region had a lower degree of resilience. The study places greater emphasis on deciphering region-specific drivers and barriers to resilience at a further disaggregated scale for improving rural well-beings. It is construed that devising action plans emphasizing awareness, preservation of natural resources, diversification, building physical infrastructure, strengthening of grass-root institutions, and mainstreaming climate adaptation in the developmental policy is crucial for climate-resilient pathways. [ABSTRACT FROM AUTHOR]

Climate change poses major challenges to agricultural systems in drought prone regions of the world especially in the areas with high poverty, lack of irrigation facilities and low productivity. Towards this it is essential to understand the climate change perceptions, adaptation practices and barriers to effective adaptation at household and community level. This paper using household surveys and focus group discussions in one of the drought prone areas of Odisha in India, explores various aspects of perception on climate change and barriers to adaptation. It also analyses the accuracy of perceptions based on rainfall data from nearest meteorological stations. The study reinforces the argument by earlier studies that the perception by people simply cannot be wrong because they may have a just a low correlation with underlying meteorological data. Results suggest that farmers increasingly perceive the changes in the rainfall and temperature patterns. However, when compared with the trend in actual rainfall data, perceptions on rainfall are found to more closely align with the results from the nearest station as compared to the station farther from it. Analysis revealed that seasonal rainfall variability has a profound influence on the farmers' perceptions on climate change and drought in the study region. Farmers' are still dependent on the traditional forecasting system because of the lack of access to modern climate forecasting and tailored information for agricultural practice. Although farmers in the study region are already adapting to the changing climate, the study finds that while lack of access to water and irrigation, information on climate change adaptation and early warning systems are major barriers to adaptation at the household level lack of government intervention, lack of knowledge on drought resistant crops and varieties and lack of renovation of water bodies and irrigation were mentioned as the major barriers at the community level. Findings from the paper argue for better adaptation planning at the local level incorporating local level perceptions and barriers to adaptation. In such areas local level planning can be crucial in enhancing the adaptive capacity of the farmers. [ABSTRACT FROM AUTHOR]

(Global) value chains have contributed to agricultural technology adoption in transition countries and food export commodities in developing countries through various forms of vertical coordination, often associated with FDI. Adoption of modern technologies in agriculture is crucial for improving the productivity and welfare of poor farmers. Much less is known about how (global) value chains do (not) affect technology transfer and/or adoption in staple food chains in developing countries, and the role that FDI plays. Our paper analyses the role of value chains and FDI in farm-level technology adoption with the use of panel data from representative farm surveys in 2008 and 2015 (a period of rapid growth) in the dairy sector in India (Punjab). We find important increases in the adoption of some technologies among traditional and poor dairy farms. However, the role of vertical coordination in value chains in stimulating technology adoption among these traditional dairy farmers seems to be minor, both for domestic and for FDI companies. At the same time a sub-sector of dynamic modern dairy farms has emerged. These farms are much larger, use only modern technology, and are fully integrated in vertically coordinated value chains that support these modern farms' management and investments. [ABSTRACT FROM AUTHOR]

Nearly half of the global populations live in rural area. For the developing country like India, this figure is nearly 70% of the mass population. Currently, major challenges faced by the rural community are lack of clean water, food, good health, energy, stable economy and environment. Nanotechnology and nanoscale materials have the potential for the solutions of many significant challenges faced by our society. This review paper summarized some of the most promising and important nanotechnology applications in agriculture and food, clean water, energy, environment, human health and other consumer products. The review has been concluded with the potential risks of nanomaterials, technical and financial challenges for the implementation of nanotechnology in the rural community developments. [ABSTRACT FROM AUTHOR]

Drought is an important downside risk in Indian agriculture; and the spatial differences in its intensity and probability of occurrence are considerable. To develop strategies to manage the risk of drought, and to coordinate and implement these strategies, it is essential to understand the variation in drought risk across geographical or administrative levels. This paper, using a multilevel modeling approach, decomposes the variation in drought risk across states, regions, districts, villages and households, and finds it disproportionately distributed. About half the variation is attributed to between-individual (i.e., household) differences and the rest to between-population differences, mainly to states and villages. These findings suggest the potential for a critical role of states (policies) and local institutions (communities) in enhancing resilience of agriculture to droughts through the correct targeting of policies and support for the most appropriate geographic level. [ABSTRACT FROM AUTHOR]

In India, farming is the primary source of income for many families. Following each harvest, a huge amount of biomass is generated. These are generally discarded as 'agrowaste,' but recent reports have indicated several beneficial uses for these biomasses and their ashes. However, before the utilization of biomass ashes (BMAs), their chemical and physical properties need to be investigated (characterized) so as to utilize their potential benefit to the fullest. In this paper, eight different biomass ashes (soybean plant ash, mustard plant ash, maize ash, groundnut plant ash, cotton plant ash, wheat plant ash, pigeon peas ash, and groundnut shell ash) were characterized, and their chemical properties are discussed. Surface chemical composition analysis, proximate analysis, and ultimate analysis were performed on all BMA samples, and properties such as porosity, particle density, bulk density, point of zero charge, BET surface area, water-absorption capacity, and bulk parameters such as surface pH and surface charges were determined. BMAs were characterized by SEM and FTIR. The surface areas of biomass ashes vary from 1.9 to 46 m/g, and point of zero charge for all BMAs exceed 9.8, which confirmed the alkaline nature of these samples. Based on the chemical composition, BMAs are categorized into four types (S, C, K, and CK), and their utilization is proposed based on the type. BMAs find applications in agriculture and construction industries; glass, rubber, and zeolite manufacturing; and in adsorption (as a source of silica/zeolites). The paper also discusses the research challenges and opportunities in utilization of BMAs. [ABSTRACT FROM AUTHOR]

The narrow dependence on paddy and wheat cultivation along with incentives such as free electricity and water has resulted in overuse of pumps and significant depletion of ground water resources in Punjab. Stagnating yields and soil degradation due to intensive cropping pose a significant threat to long-term agricultural productivity in the state. In this paper, we focus on the farmers' decision-making process and identify policy changes that are required to incentivize farmers to cultivate a diverse portfolio of crops and reduce dependence on paddy and wheat. Using data from four representative districts of Punjab, we quantify the impact of introducing alternate crops on the farmers' profitability. Our analysis shows that incorporating horticultural crops in the mix increases net expected returns from cultivation for all land size classes. We also find that including alternate crops reduces water usage by up to 30 % for most farmers, while increasing the labor and working capital requirements. We also examine risk factors-price, labor, credit and yield risk-that might be contributing to the status quo and suggest risk mitigation strategies to promote crop diversification. [ABSTRACT FROM AUTHOR]

Climate change adds another dimension of challenges to the growth and sustainability of Indian agriculture. The growing exposure to livelihood shocks from climate variability/change and limited resource base of the rural community to adapt has reinforced the need to mainstream climate adaptation planning into developmental landscape. However, a better understanding of micro-level perceptions is imperative for effective and informed planning at the macro-level. In this paper, the grass-root level perspectives on climate change impacts and adaptation decisions were elicited at farm level in the Moga district of Punjab and Mahbubnagar district of Telangana, India. The farmers opined that the climatic variability impacts more than the long-term climate change. They observed change in the quantum, onset and distribution of rainfall, rise in minimum as well as maximum temperature levels, decline in crop yield and ground water depletion. The key socio-economic effects of climate change included decline in farm income, farm unemployment, rural migration and increased indebtedness among farmers. In order to cope with climate variability and change thereon, farmers resorted to adaptation strategies such as use of crop varieties of suitable duration, water conservation techniques, crop insurance and participation in non-farm activities and employment guarantee schemes. Farmers’ adaptation to changing climate was constrained by several technological, socio-economic and institutional barriers. These include limited knowledge on the costs-benefits of adaptation, lack of access to and knowledge of adaptation technologies, lack of financial resources and limited information on weather. Besides, lack of access to input markets, inadequate farm labour and smaller farm size were the other constraints. Further, on the basis of the grass-root elicitation a ‘Need-Based Adaptation’ planning incorporating farmers’ perceptions on climate change impacts, constraints in the adoption of adaptation strategies and plausible adaptation options were linked with the most suitable ongoing programmatic interventions of the Government of India. The study concluded that micro-level needs and constraints for various adaptation strategies and interventions should be an integral part of the programme development, implementation and evaluation in the entire developmental paradigm. [ABSTRACT FROM AUTHOR]

Food security is a crucial requirement in today's world to meet the dietary needs of individuals. As the population continues to grow, the demand for food will increase by 70 to 100 percent by 2050. Therefore, there is an urgent need to develop an approach that can assist farmers in predicting crop yield accurately and in a timely manner before crop harvesting. In this direction, the present study proposed a nature-inspired optimized hybrid convolutional neural network with bidirectional long short-term memory to extract the nonlinear complex relationships among crop attributes. This hybrid deep learning model was optimized using a particle swarm optimization approach to automate the selection of appropriate hyperparameters for wheat yield prediction. The proposed model was developed to estimate wheat yield in a major wheat-producing state in India from 2000 to 2018 using the temporal and spatial characteristics of wheat crops. The experiment was conducted by integrating the historical yield, meteorological data, remote sensing-derived indices, and soil parameters from the October to April season. Furthermore, we also evaluated the performance of the proposed model in terms of the mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE) with publicly available datasets such as Agro_data, Soybean_data, and FAO_data. The experimental results showed that the proposed PSO-CNN-Bi-LSTM method achieved an MAE of 0.39, MSE of 0.18, and RMSE of 0.42 (tonnes/ha) and outperformed existing CNN, LSTM, CNN-LSTM, CNN-Bi-LSTM, CNN-LSTM-PSO, CNN-Bi-LSTM-BO-GO, and neural network methods. Our findings demonstrated that the proposed methodology could be a promising alternative for predicting crop yield. [ABSTRACT FROM AUTHOR]

Agricultural practices naturally interject greenhouse gases (GHGs) into the atmosphere; consequently, analyzing decadal variation in N2O, CO2, and CH4 is indispensable over a major rice cultivating area (West Bengal) in subtropical India. Analysis of EDGAR data shows an unremitting increase in GHGs in the study region over two decades. The increasing global warming makes us revisit climate-resilient strategies to mitigate the risk of climate change on paddy production in agrarian economies. However, adapting to these strategies and impact assessment is necessary to comprehend their productiveness and further policy recommendation. Therefore, this study identifies the determinants of adaptation and measures the impact of these strategies on farm performance (yield and net income) and food security of smallholder paddy farmers in India. We used primary data from 612 paddy farmers from 20 villages in the Hooghly district, West Bengal, India. The study used probit regression analysis to identify adaptation constraints and Multinomial Logistic Regression (MLR) to determine the change in the direction and magnitude of the determinants of adaptation strategies. The study also used the Propensity Score Matching (PSM) approach to estimate the causal impact of climate adaptation on paddy yield, net income, and food security. Probit model outcomes identified that education level, cooperative membership, access to extension services, and institutional credit adoption positively and significantly impacted climate change adaptation across smallholders. MLR analysis identified that the direction and magnitude of the discussed determinants changed with different combinations of adaptation choices. The PSM results estimated that adaptation of climate-resilient strategies impacted paddy productivity, net income, and food security positively and significantly. In addition, paddy farmers using a more significant number of climate adaptation strategies experienced better yield, revenue, and food security than those farmers using fewer adaptation strategies. [ABSTRACT FROM AUTHOR]

Water quality of a river is a function of surrounding environment and land use due to its connectivity with land, resulting in pollutants finding their way through land. This necessitates a spatially explicit study of river ecology. The paper presents a pioneer study to establish and explore the linkage between land use and water quality of river Ganga in Varanasi district. The land use land cover (LULC) map of 20 km of river stretch for buffer radii of 1000 m in Varanasi revealed that riparian vegetation is negligible in the district. The hierarchical cluster analysis of LULC data suggested that there are two major land use categories, viz., urban and agriculture. The land use wise principal component analysis (PCA) suggested that urbanized areas are major contributor of metals, whereas agricultural land contributes organic matter into the river. The Spearman correlation study revealed that with rising urbanization, the pollutant load into the river increased compared to that from agricultural land use. The statistical analysis of the data clearly concluded that water quality of river Ganga at Varanasi was a function of adjacent land use. The study provides an insight anticipating the Indian government to embrace the relationship of land use to river water quality while formulating policies for the upcoming River Regulation Zone. [ABSTRACT FROM AUTHOR]

This paper attempts to estimate and identify the regional disparity of India's agricultural labour productivity growth by decomposing it into technical changes, efficiency changes and input accumulation per worker during the 2000–2016, the period marked with the launch of employment guarantee programme in rural India in 2006 and 2007. The study observes a significant growth in agricultural labour productivity despite variation in their sources, across the states in India during the sample period. While growth of India's agricultural labour productivity mainly depended on the accumulation of inputs and technical progress, efficiency changes contributed more towards regional disparities in agricultural productivity growth. Improving efficiency to promote TFP growth is important for agricultural labour productivity growth for the low-performing states. [ABSTRACT FROM AUTHOR]

In the context of climate change, the Indian agricultural sector treads in a certain duality between promoting food security in response to the increasing population, but at the same time in ensuring environmental sustainability, and sustained economic growth, especially in developing countries like India. The concept of Climate Smart Agriculture (CSA) emerged from the recognition of this duality. Using the Indian Social Accounting Matrix (SAM) 2017–18, the economy-wide effects arising out of agricultural interventions were estimated, keeping accord with the impacts on sectoral outputs and household incomes from the adoption of varying CSA interventions such as Conservation Agriculture, System of Rice and Wheat Intensification (SRI-SWI) and Natural Farming, fitting the three-pillared criterion of CSA—(1) Productivity (2) Adaptation and (3) Mitigation. Additionally, a shift in cropping patterns from Paddy and Wheat to less emission-intensive crops was also studied. Results show that SRI-SWI provides the highest economy-wide impacts while accounting for lower GHG and water footprint. Alternative crops such as Maize, Sorghum, and Millet have minimal increase in income and output effects while having lower water and carbon intensity compared to rice and wheat. The current study would sensitize policymakers to prioritize suitable policy and institutional measures for upscaling climate smart interventions in India. [ABSTRACT FROM AUTHOR]

In the recent era of globalization and climatic uncertainties, farmers are continuously adopting new strategies and crops to ensure substantial production and income. However, these adaptations always need to be based on systematic decisions; otherwise, the entire environment may degrade. The present study is one of such attempts to understand where to adopt an emerging crop (Maize) in lower Gangetic West Bengal. The decision on suitable sites was taken based on the analysis of fourteen important indicators in a GIS integrated fuzzy-MCE environment aided with different geostatistical interpolation models. The output shows that 18 and 30% of the total agricultural area of the study region is highly suitable and suitable for Maize cultivation following the FAO-based suitability classification arena. Spatially, it can be said that the farmers from a few blocks of the study area, namely Chanchal-I, II, Harishchandrapur I, II, Lalgola, Bamangola, Ratua-I, II, Suti, Burwan, etc., can ensure a sustainable profit by adopting Maize cultivation. [ABSTRACT FROM AUTHOR]

Population in South Asia is increasing ever than a faster rate, subsequently; food security, climate change, and capital intensive agro farming techniques are the prevailing challenges in this region. This is a tri-country penal analysis, Pakistan, India, and Bangladesh, and the study covers the data throughout (1973–2020). This study has used modern farm input data besides demographic variables in the study. In this study, we use panel data set, ARDL (PMG) approach, autoregressive distributed lag model pooled mean group, which is an extensively dynamic modeling technique for heterogeneous data. The results of the study explore that transition in the demographic pattern in Pakistan, India, and Bangladesh is the real cause of low crop productivity and land intensification. Technology innovation is the only ray of hope to fulfill the food demand of the future ahead and climate agriculture practices can hamper the further deterioration of the small farmlands. [ABSTRACT FROM AUTHOR]

Leptospirosis, a formidable zoonotic threat spawned by Leptospira, plagues tropical and subtropical realms. This study delves deep into tropical Indian states, namely, Kerala, Gujarat, Karnataka, Maharashtra, and Tamil Nadu, unraveling the dynamics of leptospirosis through a comprehensive mathematical model that embraces temperature-driven growth rates of Leptospira. Sensitivity analysis and parameter estimation techniques fortified the model's accuracy, unraveling the factors shaping leptospirosis transmission. Notably, the numerical results highlight the significant impact of rainfall, fishing, climate, mining, agriculture, and cattle farming on leptospirosis prevalence in the endemic states of India. Finally, our study urges resolute preventive action to control and combat leptospirosis in India. Strengthening surveillance, impactful awareness campaigns, targeted interventions, and improved hygiene practices among high-risk individuals are vital. Embracing these proactive strategies will alleviate the burden of leptospirosis and enhance public health in India and beyond. [ABSTRACT FROM AUTHOR]

Increased occurrences of climate extremes are one of the primary indicators to identify climate change. From agricultural perspectives, vulnerability is the proneness of a region to extreme climate events which cause crop loss, yield reduction eventually leading to the decline in farmers income. An extreme weather event becomes a disaster when the society or ecosystems are unable to cope with it effectively. The vulnerability of agriculture in the Lower Vellar River sub-basin has been quantitatively assessed which can help provide guidelines to improve the adaptive capacities of the farming communities in the sub-basin. The vulnerability has been assessed with a total of 36 indicators categorised under three dimensions, namely sensitivity, exposure, and adaptive capacities, for the years, 2011 and 2015 which experienced extreme precipitation events, Cyclone Thane and the Deep Depression of Bay of Bengal, respectively. The vulnerability index results highlight that the sensitivity of coastal blocks, namely Parangipettai, Keerapalayam, and Mel Bhuvanagiri to precipitation extremes is higher when compared to the interior blocks Nallur, Mangalur, and Virudhachalam. The exposure of the coastal blocks to the extreme events in the years 2015 and 2011 was high, with the year 2011 having lower exposure values than 2015. The main cause for low exposure values in 2011 for the coastal blocks can be attributed to high temperature trends and subsequent high exposure index values in Mangalur which is an interior block in the basin. The adaptive capacities in the coastal blocks are very less making them highly vulnerable. In general, the coastal regions have medium–high vulnerability than the interior regions which have low-medium to medium vulnerability. [ABSTRACT FROM AUTHOR]

Various persistent organic pollutants (POPs) are used in pharmaceuticals, personal care products, dyes, pesticides, and electrical devices to meet significant demand. Natural POPs, as well as anthropogenic synthetic carbon-based compounds, are hazardous pollutants. POPs are trendy but also toxic to living organisms. Bioaccumulation via the food web makes these susceptible to diseases and multiorgan dysfunction. The persistence, bioaccumulation potential, and adverse effects of POPs on humans and wildlife make them a major environmental and public health concern in India. POPs are a problem in India due to the country's industrial sector, agricultural methods, and poor waste management. Protecting the Indian population's health and preserving the country's various ecosystems depend on finding a solution to the POP's problem. The current review article discusses POPs in terms of estimation techniques such GC–MS and the compiled effects of POPs on biota. Separating and identifying volatile organic molecules is often done using GC–MS, while non-volatile and polar chemicals are analysed using LC–MS. Separation methods such as HPLC are flexible and can be modified with a wide range of analytes. Concentrating and purifying analytes before instrumental analysis is possible using sample preparation and extraction technologies like CPE, SPE, and SPME. Strong extraction strategies like ACE and QuEChERS aim to streamline and simplify the procedure. MAE employs microwave radiation to heat samples, rapidly accelerating the extraction process. The article intends to discuss practical techniques for removing POPs from the environment, such as catalysis, coagulation, adsorption, and biological removal. [ABSTRACT FROM AUTHOR]

Bactrocera dorsalis (Hendel) is one of the important pests of great economic significance causing serious fruit and vegetable damage in tropical and subtropical areas of the world occurs. This study aimed to investigate the effect of high fat diet on various physiological traits of females and a shift of sex ratios in favour of males to benefit biological control of this pest species. B. dorsalis individuals were collected from Punjab Agricultural University (PAU), Ludhiana, Punjab and the effect of coconut oil-based artificial diets differing in their relative levels of fat (5%, 10% and 20%) on stage-specific development, survival, hatchability were analysed at 21 °C, 65% Relative Humidity. The male: female (M/F) sex-ratios from individuals emerging from each of these diets were also scored. The findings of the study showed that B. dorsalis larvae and adults were able to survive on all three diets, although the stage-specific development and survival varied with High Fat Diet (HFD) concentration. Diet with 20% fat concentration showed most delayed development relatively to 10% and 5% concentration diet. The laboratory test showed that ingestion of fat significantly increased the development time (egg-larvae-pupae to adult) and caused increased mortality in pre-adult stages. A decrease in percentage pupation and adult emergence was also observed. Moreover, the data showed a significant shift in M/F ratios for regular food, HFD 5%, and HFD 10% with 46.7%, 45.7%, and 45.9% respectively and all female biased. Only HFD 20% produced male biased ratio of 74.4%. Adults fed on 20% HFD produced significantly fewer eggs as well as reversed the male: female sex ratio from 1:1.28 to 2.9: 1. The HFD diet elicited a higher adult lipid content, lower pupal production, adult emergence, and longer development, significantly affecting physiology of fruit flies. Field efficacy trials showed significant results with bait trap method and significantly increased the fruit yield by 51.09 Kg/tree as compared to the control. Hence, the present study illustrated the potential of HFD which can be exploited in integrated pest management strategies for capturing fruit fly pests of economic significance. [ABSTRACT FROM AUTHOR]

Flooding events are determining a significant amount of damages, in terms of economic loss and also casualties in Asia and Pacific areas. Due to complexity and ferocity of severe flooding, predicting flood-prone areas is a difficult task. Thus, creating flood susceptibility maps at local level is though challenging but an inevitable task. In order to implement a flood management plan for the Balrampur district, an agricultural dominant landscape of India, and strengthen its resilience, flood susceptibility modeling and mapping are carried out. In the present study, three hybrid machine learning (ML) models, namely, fuzzy-ANN (artificial neural network), fuzzy-RBF (radial basis function), and fuzzy-SVM (support vector machine) with 12 topographic, hydrological, and other flood influencing factors were used to determine flood-susceptible zones. To ascertain the relationship between the occurrences and flood influencing factors, correlation attribute evaluation (CAE) and multicollinearity diagnostic tests were used. The predictive power of these models was validated and compared using a variety of statistical techniques, including Wilcoxon signed-rank, t-paired tests and receiver operating characteristic (ROC) curves. Results show that fuzzy-RBF model outperformed other hybrid ML models for modeling flood susceptibility, followed by fuzzy-ANN and fuzzy-SVM. Overall, these models have shown promise in identifying flood-prone areas in the basin and other basins around the world. The outcomes of the work would benefit policymakers and government bodies to capture the flood-affected areas for necessary planning, action, and implementation. [ABSTRACT FROM AUTHOR]

More than 60% of India's population relies on agriculture as their primary source of income, making it the nation's most important economic sector. Rice husk (often abbreviated as RH) is one of the most typical by-products of agricultural production. Every five tonnes of rice that is harvested results in the production of one tonne of husk. The concept of recycling and reusing waste from agricultural production has received interest from a variety of environmental and industrial perspectives. A wide variety of nanomaterials, including nano-zeolite, nanocarbon, and nano-silica, have been discovered in agro-waste. From rice cultivation to the finished product, there was a by-product consisting of husk that comprised 20% of the overall weight, or RH. The percentage of silica in RH ash ranges from 60 to 40%, with the remaining percentage consisting of various minerals. As a direct consequence of this, several distinct approaches to generating and extracting nanomaterial from rice husk have been developed. Because it contains a significant amount of cellulose and lignin, RH is an excellent and economical source of carbon precursor. The goal of this chapter is to produce carbon-based nanomaterials from RH. [ABSTRACT FROM AUTHOR]

Mitigating the atmospheric greenhouse effect while enhancing the inherent soil quality and productive capacity is possible through soil carbon (C) sequestration, which has a significant potential to counteract the adverse effects of agroecosystem level C emission through natural and anthropogenic means. Although rice is the most important food in India, feeding more than 60% of the country's population, it is commonly blamed for significant methane (CH4) emissions that accelerate climate change. Higher initial soil organic matter concentrations would create more CH4 under the flooded soil conditions, as reducible soil C is a prerequisite for CH4 generation. In India, rice is generally cultivated in lowlands under continuous flooding. Less extensive organic matter breakdown in lowland rice agroecosystems often significantly impacts the dynamics of soil active and passive C pools. Change from conventional to conservation agriculture might trap a significant quantity of SOC. The study aims to investigate the potential of rice-based soils to sequester C and reduce the accelerated greenhouse effects through modified farming practices, such as crop residue retention, crop rotation, organic farming, varietal selection, conservation agriculture, integrated nutrient management, and water management. Overall, lowland rice agroecosystems can sequester significant amounts of SOC, but this potential must be balanced against the potential for CH4 emissions. Management practices that reduce CH4 emissions while increasing soil C sequestration should be promoted and adopted to maximize the sustainability of rice agroecosystems. This review is important for understanding the effectiveness of the balance between SOC sequestration and CH4 emissions in lowland rice agroecosystems for adopting sustainable agricultural practices in the context of climate change. [ABSTRACT FROM AUTHOR]

Earthworms encompass significant soil faunal biomass and have tremendous potential to provide vital ecosystem services. Earthworms are considered bioindicators of chemical contaminants and can provide early warnings of ecosystem deterioration. Studies pertaining to the accumulation of pesticide residues in earthworm in biomass in agrarian ecosystems are scarce. The Kuttanad agroecosystem (KAE), situated on the southwest coast of India, is one of the few regions globally supporting farming on land below the mean sea level. This investigation was conducted to assess the bioaccumulation of pesticide residues in earthworms from the KAE. The earthworms species Glyphidrilus annandalei collected from agricultural soils of the study area were analyzed for the presence of pesticides residues such as α-BHC, γ-BHC, atrazine, heptachlor, α-chlordane, γ-chlordane, 4,4-DDE, 4,4-DDD, 4,4-DDT, β-endosulfan, and endrin ketone in their biomass. Analysis of the earthworm samples using a gas chromatograph revealed the presence of ten pesticide residues with notable concentrations (α-BHC, 0.36 ng/g; γ-BHC, 0.41 ng/g; heptachlor, 0.10 ng/g; atrazine, 0.89 ng/g; α-chlordane, 0.07 ng/g; γ-chlordane, 0.10 ng/g; 4,4-DDE, 0.05 ng/g; 4,4-DDD, 0.11 ng/g; 4,4-DDT, 0.31 ng/g; β-endosulfan, 0.19 ng/g; and endrin ketone, 0.13 ng/g). Six groups of pesticide residues are ΣBHC, ΣDDT, atrazine, Σchlordane, endrin ketone, and β-endosulfan were observed during bioaccumulation factor analysis, and the results show the following trend: atrazine > ΣBHC > ΣDDT > Σchlordane > Σendosulfan > Σendrin. As earthworms are a crucial component of this region's food chains, bioaccumulation of pesticide residues in earthworms can pause adverse consequences. Increasing trends in pesticide application in the KAE and bioaccumulation of pesticide residues in earthworm biomass can affect the entire food web. [ABSTRACT FROM AUTHOR]

Climate variability is directly or indirectly affecting crop production and threatens the food security of livelihood. The Cauvery delta region (CDR) is the agricultural protective zone of Tamil Nadu state, the crop production in this region is vulnerable to climate change impacts. In this study, the future climate change impact of black gram yield has been assessed using the DSSAT-CROPGRO model in the CDR. The model was calibrated during the 2018 sowing period and validated during 2019 cultivation practices for determining the genetic coefficient of the black gram cultivar VBN6. The RMSE values of observed and simulated yield were 910 and 890 kg ha−1 respectively. RegCM4 model is used for the simulation of Vigna mungo yield under RCP 4.5 and 8.5 scenarios during the 1985–2015 (baseline), 2020–2039 (near-century), 2040–2069 (mid-century), and 2070–2099 (end-century). The results reveal that simulation of black gram yield change could be around 34%, 52%, and 25% during the near-century, mid-century, and end-century respectively for RCP 4.5 based on the baseline period. Adjusting the sowing date is evaluated as an adaptation option in future climate change scenarios, and results revealed that early sowing in the winter season could be the best suitable growing period for better yield in this region. [ABSTRACT FROM AUTHOR]

Integrated fish farming focuses on a variety of agricultural possibilities mainly emphasizing fish farming in conjunction with crop and/or livestock farming. The present study was carried out to examine the influence of such an integrated farming system (IFS) on the livelihood of farmers in the state of Manipur, which is in the Northeastern region of India. Changes in the level of livelihood security and livelihood status were assessed for a sample of 300 farmers, who have adopted four major types of IFS, viz, fish-vegetable crop farming based IFS, fish-paddy crop farming based IFS, fish-vegetable-oilseed crop farming based IFS, and fish-crop-livestock based IFS. The adoption of IFS has increased farmers' economic stability and social standing, and the system is found to be economically feasible and sustainable. Farmers adopting fish-crop-livestock based IFS (primarily fish-piggery-poultry based IFS) have the highest food and nutrition security and overall livelihood security. Out of five livelihood assets (physical, social, financial, human, and natural), IFS has influenced farmers' financial assets holding the most. An overall improvement of 26.83% in the respondents' standard of living is realized by the farmers when they adopted the IFS. Considering the consequences of different types of IFS on farm livelihoods, the most beneficial models like fish and/or crop and/or livestock based IFS may be upscaled as these integrations result in the effective use of the waste products from crop and/or livestock system to produce diverse fish varieties. It has also improved farmers' livelihoods by generating consistent income and employment opportunities. [ABSTRACT FROM AUTHOR]

Spatial distribution of rice cultivated area and its future projections are indispensable for providing food security, irrigation water management, greenhouse gas emission, ecosystem, and environmental protection. Accurate simulation of future rice cultivation is challenging in the context of climate change involving spatial modeling. This study introduced a hydrological data-based prediction approach integrating cellular automata-Markov chain model with multi-criteria decision analysis. Three decadal transitions of different land use land cover (LULC) in an agricultural intensified canal irrigated area were analyzed using Random Forest algorithm. The Landsat satellite imageries were classified with an overall accuracy of more than 90%. The results revealed that the area under water sources, plantation, and fallow land decreased by 53.1%, 33.9%, and 63.6%, respectively, while habitation and Boro rice cultivated area increased significantly by 1243.3% and 211%, respectively, during 1988–2018. The kappa coefficient (0.86), allocation disagreement (10.9%), and quantity disagreement (1.16%) revealed good agreement between the predicted and classified LULC map of 2018. The probable distribution of the LULC map in 2028 and 2038 indicated 54.5% and 59.3% of the command area under Boro rice cultivation as a dominant land cover, respectively. Further, the predicted LULC map anticipated that Boro rice cultivation was likely to expand at the expense of plantation and fallow land which may lead to massive water scarcity in the near future. The proposed approach covered up the limitations of spatial modeling on agricultural growth trends by integrating significant driving factors with quantitative and spatiotemporal aspects of LULC transition. These findings will provide a baseline map for sustainable policy reformation and contemplation on the repercussions that local LULC transition has on global environmental change. [ABSTRACT FROM AUTHOR]

Potentially toxic metals (PTMs) contamination in the soil poses a serious danger to people's health by direct or indirect exposure, and generally it occurs by consuming food grown in these soils. The present study assessed the pollution levels and risk to human health upon sustained exposure to PTM concentrations in the area's centuries-old glass industry clusters of the city of Firozabad, Uttar Pradesh, India. Soil sampling (0–15 cm) was done in farmers' fields within a 1 km radius of six industrial clusters. Various environmental (geo-accumulation index, contamination factor, pollution load index, enrichment factor, and ecological risk index) and health risk indices (hazard quotient, carcinogenic risk) were computed to assess the extent of damage caused to the environment and the threat to human health. Results show that the mean concentrations of Cu (33 mg kg−1), Zn (82.5 mg kg−1), and Cr (15.3 mg kg−1) were at safe levels, whereas the levels of Pb, Ni, and Cd exceeded their respective threshold limits. A majority of samples (88%) showed considerable ecological risk due to the co-contamination of these six PTMs. Health risk assessment indicated tolerable cancer and non-cancer risk in both adults and children for all PTMs, except Ni, where adults were exposed to potential threat of cancer. Pearson's correlation study revealed a significant positive correlation between all six metal pairs and conducting principal component analysis (PCA) confirmed the common source of metal pollution. The PC score ranked different sites from highest to lowest according to PTM loads that help to establish the location of the source. Hierarchical cluster analysis grouped different sites into the same cluster based on similarity in PTMs load, i.e., low, medium, and high. [ABSTRACT FROM AUTHOR]

Agriculture is the primary source of income in the Sub-Himalayan Jalpaiguri District; therefore, identifying the optimal use of existing agricultural land is crucial. The primary objective of this study is to identify potential agricultural sites in the Jalpaiguri District for the sustainable development of the region. About eleven parameters have been considered using the multi-influencing technique in combination with remote sensing (RS) and geographic information system to delineate and model potential agriculture sites. The final agriculture suitability map was created using the 'Weighted Overlay technique,' and the final output can be categorized into five classes, i.e., highly suitability (424.3 km2), moderately suitability (1191.8 km2), marginally suitable (1141.4 km2), currently not suitable (567.1 km2), and permanently not suitable (60.8 km2). Besides, the results have been thoroughly verified using Google Earth images, in comparison with Landsat 8 output, and field visits using GPS to increase the reliability of the results. The finding reveals potential outcomes for agricultural activity; however, building a sustainable management strategy and resilient farming practices should be adopted to boost the region's agricultural output. Farmers, regional planners, and government officials can use the current agriculture suitability map to make comprehensive judgments for the region, such as determining the potential for agriculture sites, improving agricultural growth, and promoting self-reliant local economies. [ABSTRACT FROM AUTHOR]

The reclamation potential of Halimeda microloba in sodic soil was studied. The soil sample was collected and soil quality parameters were studied before and after reclamation. The seaweed was collected from the southeast coast of India and it was identified as Halimeda microloba. In addition, various commercial fertilizers including Rhizobium, Azotobacter, Azospirillum, Trichoderma, Pseudomonas, and gypsum were purchased, isolated, and identified for their influential reclamation. A pot experiment was carried out and after 1 month of treatment, the soil parameters were accomplished. The Vigna radiata (green gram) seeds were sought, monitored, and examined periodically. After reclamation treatment, the pH was greatly reduced from 9.8 to 8.0 in Halimeda microloba treatment. Meanwhile, electrical conductivity and exchangeable sodium percentage levels were diminished from 713.8 to 234.3 µs/cm and 62 to 21% respectively in gypsum treatment. Furthermore, the calcium content in gypsum was found to be 5010 mg/100 kg, and in Halimeda microloba of about 3772 mg/100 kg. Similarly, microbial biomass carbon was found to be a notable increase (P < 0.05) in gypsum and seaweed treatment as compared to control. There is a substantial growth of bacterial and fungal populations in seaweed and gypsum-treated soil. However, the individual use of different microorganisms has shown limited outcomes on the sodic soil. Indeed, the treatment with gypsum/seaweed, and seaweed shows promising in plant growth, soil enzyme activity (dehydrogenase), and enhanced nutrient uptake in the soil gypsum treatment. Finally, this study is the first attempt as proved as Halimeda microloba is an efficient biological candidate for ameliorate sodic soil transformation with a wide range of agricultural benefits. [ABSTRACT FROM AUTHOR]

Molecular phylogenetic species recognition is essential in precisely differentiating the morphologically cryptic species of Metarhizium and Beauveria. These fungal entomopathogens can be an alternative to conventional synthetic insecticides and are emerging as effective biocontrol agents against sucking insect pests. In the present study, we identified Metarhizium and Beauveria species in diverse agricultural and semi-natural ecosystems of south Karnataka State of India using phylogenetic markers and evaluated their pathogenicity against Scirtothrips dorsalis. Based on morphological characters, 34 Metarhizium and 15 Beauveria isolates were identified in agricultural and semi-natural ecosystems. Phylogeny of Metarhizium was resolved using markers, TEF1-α, MzIGS3, and ITS rDNA to two species, M. pingshaense and M. guizhouense and Beauveria was resolved using markers, TEF1-α, Bloc, and ITS rDNA to a single species, B. bassiana based on phylogenetic optimality criteria. Isolates of M. pingshaense and B. bassiana resolved three phylogenetic clades each and M. guizhouense resolved one phylogenetic clade. Phylogenetic clades of M. pingshaense and B. bassiana exhibit intraclade variation and are not associated with crop plant or host insect. Laboratory bio-efficacy studies of 49 isolates of Metarhizium and Beauveria against adult S. dorsalis showed that pathogenicity varied significantly among isolates. Greenhouse bioassays revealed the high virulence of B. bassiana isolates over M. pingshaense isolates against S. dorsalis. Low LC50 value of 2.8 × 104 conidia/ml was obtained for B. bassiana GKVK 01_06 isolate against S. dorsalis. Our results indicate that indigenous B. bassiana isolates have strong potential in the management of S. dorsalis. [ABSTRACT FROM AUTHOR]

This current study examines the impact of renewable energy consumption, agriculture, and globalization on carbon emissions in India over the period from 1980 to 2018. For long-run estimates, we apply Gregory-Hansen's co-integration test, bootstrap ARDL approaches, fully modified ordinary least squares, and dynamic OLS. The empirical results of long-run estimates indicate that a 1% increase in renewable energy consumption, agriculture, and economic globalization will increase carbon emissions by 0.764%, 1.675%, and 0.517%, respectively. Moreover, this study confirms the detrimental effect of these variables on carbon dioxide emissions. Economic globalization coefficients indicate that the scale effect is valid in India. The 2002 economic crisis slowed down the country's growth rate, which reduced the ecological pollution. Several policy recommendations are derived from the empirical findings. [ABSTRACT FROM AUTHOR]

The rapid collection and development of multimedia data and devices allows agricultural monitoring to be automated. Crop classification using aerial images is the challenge of identifying the appropriate crop type planted on a certain area of land. These approaches, however, have several drawbacks that may inhibit the performance owing to a lack of appropriate data and the lack sufficient investigation of automatic feature extraction techniques. To deal with this, an ensemble approach has been investigated using two different deep convolutional neural networks (DCNN), namely Multi-Filter Multi-Scale deep convolutional neural network (MFMS-DCNN) and a pre-trained Inception V3 architecture, for crop classification using aerial images. At the onset, the MFMS-DCNN is constructed using Convolution- batchnormalization-activation units along with max and average pooling layers. Here, input images have been down-sampled using the max- pooling operation instead of traditional image processing approaches which facilitates to extract the important features in multiple levels, and the feature maps are concatenated by embedding a step-by-step fusion approach. The pre-trained Inception V3 model is initially fine-tuned using the three datasets under consideration (publicly available Plant seedling, and two new aerial image datasets obtained from two regions across India.). Finally, two combiners (Mean rule and Product rule) are enforced to ensemble the outputs of MFMS-DCNN and pre-trained Inception V3, to give the final prediction. To validate the feasibility of the proposed method, experimentations have been performed on the considered datasets and with state-of-the-art techniques. The findings derived from these image datasets produce superior performance for the proposed schemes as compared to the state-of-the-art techniques. Apart from that, with average accuracy ≈9% to ≈99% for all datasets, the ensemble methods with both combiners are proven to be more efficient than the two individual proposed schemes (MFMS-DCNN and fine-tuned Inception V3). [ABSTRACT FROM AUTHOR]

We investigate the extent to which climate change will result in insurable and uninsurable losses for farmers in India. Changes in temperature and precipitation patterns may increase the volatility of farmers' yields, leading to rising but insurable risk. These changes may also reduce the expected yield in an 'average year', causing uninsurable reductions in the returns to farming. We use a multi-run climate model to predict the future distribution of potential yields at the district level for sixteen major crops. For the average district, we project a sharp decline in expected agricultural revenue, but small shifts in volatility. This is because weather draws resulting in extremely low agricultural returns—what had once been 1-in-100-year events—are predicted to become the norm by the end of the century. Our projections therefore imply substantial uninsurable losses from the changing climate for Indian farmers. [ABSTRACT FROM AUTHOR]

The evolution of predator–prey relationships is an important topic in primatology. Many aspects of primate society have been explained as a response to predation pressure. While predation has been discussed in broad theoretical terms, few systematically collected data exist on the subject. Furthermore, little information exists regarding the inter-male variation in responses to predators. To address this data gap, predatory dog–primate interactions were studied in a 78-member group of habituated, individually recognized Central Himalayan Langurs (CHL) (Semnopithecus schistaceus) living in a high-altitude subsistence agricultural landscape of northern India. We recorded 312 langur–dog interactions over 2 years. These predation events resulted in 15 serious attacks on adult females, infants, juveniles and sub-adults, in eight of which the prey was killed and consumed on the spot. In response to dog predation, adult males performed three types of anti-predator response behaviors: direct fighting with a predator, emitting alarm calls, fleeing and/or freezing. Differences were noted in each male's response to village dogs. The results showed that the likelihood of CHL adult males engaging in more costly counterattacks or attention getting alarm calls were better predicted by the level of investment in the group (genetic relatedness, duration of residency, social relationships), but not rank and mating rate. Long-duration resident adult males performed high and/or intermediate cost behaviors to protect vulnerable members of the group; their potential offspring, maternal siblings or cousins, and adult female social partners. Short-term residents or recent immigrant males exhibited two less energetically costly, more self-preserving behaviors, depending on their rank: (1) high-ranking short-tenure duration males, with high mating frequencies, performed flee and freeze responses; (2) low-ranking, low-mating-frequency males performed more alarm calls. Counterattacks and alarm calls were performed by adult males with relatively more experience with village dogs and were directed towards dogs with predatory histories significantly more often than dogs with non-predatory histories. Natural selection and kin selection have both contributed to the evolution of CHL anti-predator tactics. [ABSTRACT FROM AUTHOR]

Fluoride (F) in agricultural soil is increasing continuously due to injudicious application of F-laden fertilizers, causing global concern about fluorosis disease. The objective of the study was to assess F risk in humans due to soil ingestion, dermal contact, and particulate inhalation during various agricultural activities. The study also emphasized chemical fractionation, distribution, and geochemical understanding of high F incidence. Agricultural surface soil was sampled randomly from 5 km × 5 km square grids besides soil profile samples for studying the vertical distribution of F. Various F fractions in soil (1:1 soil:water ratio, calcium chloride extractable F, hot water soluble F, exchangeable F, Fe–Mn oxide bound F, organic matter bound F, residual F, and total F) were estimated using the sequential fractionation method. Multivariate geochemical analysis and soil F risk were also assessed in humans. The water soluble F (F1:1) and CaCl2 extractable F (FCa) varied between 0.11 to 6.73 mg kg−1 and 1.02 to 6.94 mg F kg−1 soil, respectively. Total fluoride (TF) however, ranged between 115 to 456 mg F kg−1. A higher average of FCa/TF moving down the soil profile indicated a propensity for F endemicity. Weathering, ion-exchange, alkalinity, and clay were found to control the soil geochemistry of the area. The F contamination index explained > 82% variance of F contamination, but the hazard quotient of F for an adult was found < 1, indicating no potential fluorosis risk in the area. This study is the first of its kind in India, where ecological risk due to F from agricultural soil was assessed in humans and will be a benchmark for future researchers. [ABSTRACT FROM AUTHOR]