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Actinomycetes are a suitable microbial group for the synthesis of lignocellulose-degrading enzymes. Enzymes that may degrade organic material, including cellulose, hemicellulose, and lignin, are released by actinomycetes. The aim of this research was to isolate actinomycetes from Rajkot, Gujarat, India’s soil and evaluate the activity of their cellulase and Xylanase enzymes. Starch Casein Agar (SCA) was used to identify a total of 30 isolates of actinomycetes. A qualitative plate assay (CMC-Na, Congo red) revealed that the highest zone of catalysis for MMD1 was 36 mm. Five strains were discovered to be effective for quantitative quantification of endoglucanase utilising filter paper and CMC as substrates: MMD1, MMD2, MMD3, MMD4, and MMD8. Following MMD 1 (endoglucanase 5.4 IU; FPase 4.4 IU), MMD 2 (endoglucanase 4.5 IU; FPase 3.4 IU) has demonstrated considerable endoglucanase and FPase activity. Beechwood xylan was used to treat sugarcane bagasse in order to test Xylanase, and 45% of the xylan (hemicellulose) fraction was obtained. MMD1 and MMD2 measured the xylanase enzyme activity (4.8IU and 4.2IU) in quantitative and qualitative assays (34 mm and 22 mm for BWX and 32 mm and 14 mm for agro-waste xylan). The strain MMD1 was identified as Streptomyces chartreusis through morphological, biochemical, and finally molecular characterization by 16S rRNA sequencing. It was then submitted to NCBI GenBank with the accession number MT254830. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is quickly emerging as a technological solution for the agriculture industry to surmount its classical challenges. Artificial Intelligence is facilitating farmers to refine their products and alleviate unfavourable impacts due to the environment. The central concern of this paper is predictive analytics to develop a machine learning model to identify and predict crop yield based on multiple environmental factors. In this paper, a hybrid learner 'RaNN' is proposed that combines the feature sampling and majority voting technique of Random Forest in-combination with the multilayer Feedforward Neural Network to predict the crop yield. Research has also ascertained the essential features responsible for accurate yield prediction. The proposed model works for rice yield prediction, one of the chief grains of India. The region chosen for the work is Punjab, which is among the largest producer states of India for rice. The dataset consists of 15 attributes comprising the weather and agriculture data collected from the Indian Meteorological Department Pune, and Punjab Environment Information System (ENVIS) Center, Government of India. The study has also made a comparative assessment of 'RaNN' with machine learning methods like Multiple Linear Regression, Random Forest, Decision Tree, Boosting Regression, Support Vector Machine Regression, Ensemble Learner, and Artificial Neural Network. Our model RaNN has listed a better prediction accuracy with minimal error among the other techniques providing a 98% correlation between the actual and the predicted yield. Abbreviations: AI – Artificial Intelligence; ANN – Artificial Neural Network; BR – Boosting Regression; Chem Fert – chemical fertilisers; DT – Decision Tree; EL – Ensemble Learner; ENVIS – Punjab Environment Information System; GBM – Stochastic Gradient Boosting Method; GPS – Global Positioning System; HMAX – highest maximum temperature in degrees C; IMD – Indian Meteorological Department; L1 – Lasso regression; L2 – Ridge regression; LMIN – lowest minimum temperature; ML – Machine Learning; MAE – Mean Absolute Error; MEVP – mean evaporation in mm; MLR – Multiple Linear Regression; MMAX – mean maximum temperature in degrees C; MMIN – mean minimum temperature in degrees C; MSSH – Mean sunshine duration in hours; MWS – mean wind speed in km/h; P1 – number of days with precipitation (0.1–0.2 mm); P2 – number of days with precipitation (greater than or equal to 0.3 mm); RaNN – Hybrid RF-ANN model; RMSE – Root Mean Squared Error; $${R^2}$$ R 2 – Coefficient of determination; RD – number of rainy days; RF – Random Forest; SVM Reg – Support Vector Machine Regression; TMRF – total rainfall per month in mm [ABSTRACT FROM AUTHOR]

The lives of rural households are predominantly based on agriculture and its allied activities. However, there are signs of an agricultural crisis resulting in dependence on alternative sources of income. This paper analyzes the factors that influence the decision of farm households to participate in non-farm activities for alternative income. The official data for two years on the Situation Assessment Survey of Agricultural Households are explored and a pooled probit regression model is run to examine the determinants of income diversification. Our findings reveal that farm income and irrigated land negatively influence diversification. However, diversification of those having lower access to farm holdings, socioeconomic deprivation, and improved access to education, informal credit, and crop insurance point out positive participation in non-farm enterprises. In addition, the population belonging to hilly and coastal plain regions and the age group 30–64 years are more likely to diversify. [ABSTRACT FROM AUTHOR]

This paper examines the significant agrarian transformations in India's Himalayan region of Kashmir, which is witnessing a rapid transition into commercial farming and, simultaneously, an increasing trend of deagrarianisation. The study uses a mixed-method approach to investigate why farmers adopt commercial farming in this region and how the processes of deagrarianisation, reconfiguration of land use, and deepening commodification have become inevitable for many small and medium-scale farmers as a result of this process. In the analysis, our empirically tested theoretical model confirms that institutional factors in terms of state support work as a significant driver toward commercialisation. We then argue that combined with the stimulus of the state, a vigorous process of neo-extractivism is being promoted, which renders the substantive changes in agrarian transitions by displacing the subsistence sector. The study highlights that this contemporary commercialisation is altering the social and physical landscape and driving broader forms of dispossession and consolidation of farmlands at micro-levels. Furthermore, it raises questions about the fundamental role of the Indian state's neo-extractivist policies in a politically contested region. [ABSTRACT FROM AUTHOR]

The paper attempts to examine the factors that influence climate-smart adaptation (CSA) strategies. The study used binary logit and multivariate probit models to understand the dynamics and factors of agricultural households' behavioural decisions on CSA strategies. Based on the results of the binary logit model, the study indicated that factors such as access to extension services and training, gender, educational level, land ownership, access to irrigation, access to credit and crop damage level positively and significantly influenced farmers' decisions to use CSA strategies. Similarly, the results of the multivariate probit model reveal that factors such as educational level, access to extension services and training, and land ownership had significant impacts on the adoption of the majority of CSA strategies. To improve the intensity of CSA strategies, the study recommends expanding training and extension services to farming masses, the expansion of irrigation facilities and weather information at the farm level. [ABSTRACT FROM AUTHOR]

Despite a plethora of emerging alternatives under the broad rubric of agroecology, sustainable transitions in Indian agriculture are caught between institutional inertia and lock-ins of its vast agricultural establishment on the one hand and a pro-active state promoting a natural farming. This push for Zero-Budget Natural Farming (ZBNF), while welcome as an alternative, also raises several questions on the nature of knowledge production and dissemination. This paper explores the knowledge politics around top-down "skilling regimes" and the principles and paradigms that emphasize social learning. We explore the multiple practices, meanings of agroecology among farmers, government agencies, and civil society organizations in the state of Gujarat. The action research included a survey of practices of 250 farmers in 12 districts, interviews, and stakeholder workshops. Insufficient investments in community-based extension mechanisms and lack of collaboration between state and civil society were features of the policy implementation. The absence of innovative platforms to facilitate knowledge dialogs and learning among actors to advance agroecology increases the gap between policy goals and its actual realization in practice. Programs for upscaling agroecology, we suggest, need to embed the diversity of technical and institutional processes through creating learning alliances to facilitate knowledge dialogs across dissimilar actors. [ABSTRACT FROM AUTHOR]

Pulses are an important protein source for vegetarian people across the world and they have a great potential to improve the human health, contribute to global food and nutritional security. Variety of pulses were grown in India with 25% of global production and 27% of global consumption. Though India is the largest pulses producer in the world, it imports around 3.5 million tons annually to meet the ever-increasing requirement. Less adoption of improved Agro techniques, low fertile soils and unpredictable environment conditions are major factor for low production of pulses. As a results of increasing population, the projected pulses requirement will be 32 million tons by 2030 which necessitate annual growth rate of 4.2%. With adoption of suitable agronomic strategies, the productivity has to be enhanced to meet the demand. This review work was done at Department of Agronomy, Agricultural College and Research Institute, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India. Collected review and research papers were screened from various data base like TNAU e library, ARCC journals, google scholar, research gate and scopus used to write this paper. This review article documented information gathered on importance of pulses, area, production and productivity of pulses. Nipping is important agronomic practices that removes the apical bud dominance and promotes the lateral growth which turn better maintenance of source and sink relationship and enhance the pulses yield. Influence of nipping on growth and yield of various pluses crops has been discussed here. [ABSTRACT FROM AUTHOR]

Purpose: This paper gives a model of collusion formation and a method of measuring the degree of it among the traders/bidders in the agricultural commodity markets in India. The important assumption is that the bidding is repetitive with a set of common bidders. The theory has been derived based on the behavior of the wholesale market of agricultural commodities in India. The paper is based on full information in the collusion formation. The paper first derives the theoretical structure of the bidders' behavior and thereafter derives a measure of collusion formation with the help of real-life data. Design/methodology/approach: The paper used the standard theory of optimization and the theory of auction and probability statistics. Findings: This is a complete information model of cartel formation. The bidding is repetitive and continues forever in discrete time. Hence bidders behavior is observable. Using the proposed method, if the APMC measures for each market and publishes on a periodic basis, say weekly basis, then it will be easier to break the collusion in the market where relative collision is present. For example, if a farmer has three options to sell in three different markets, then the published data would help them to select the market where the degree of collusion is relatively lower. Moreover, the undesirable loss can be avoided based on the right choice of market. As a result, transaction costs will be optima. Originality/value: The paper first derives the theoretical structure of the bidders' behavior and thereafter derives a measure of collusion formation with the help of real-life data. [ABSTRACT FROM AUTHOR]

Purpose: Given the dominant share of India in global production of fruits and vegetables, this paper intends to analyze the export competitiveness of India and other major food exporters in the world trade. The purpose of this study is to examine export structure, substitutability and complementarity of selected fresh and processed fruits and vegetables of top ten food exporters for the period 2010-20. Design/methodology/approach: Balassa's (1965) revealed comparative advantage (RCA) index was used to measure RCA indices of selected fruits and vegetables under study. Also, revealed symmetric comparative advantage (RSCA) and normalized RCA (NRCA) indices have been calculated. Further, Spearman rank correlation coefficients were computed to analyze changes over the study period for India and other competing countries. The export data have been sourced from UN Comtrade, an electronic database of United Nations, as well as World Trade Statistical Review, a database of World Trade Organization. The analysis was undertaken at Harmonized System (HS) four-digit classification for the period 2010-20. Findings: The results disclosed an improvement in India's comparative advantage over the period of 2010-20 in HS 07 product category, whereas the advantage ceded to other competitive nations in HS 08 product category. Further, Spearman rank correlation coefficients revealed that India faces competition from countries like China, Indonesia, Brazil, Thailand, Argentina and European Union for HS 07 product category, while countries like Mexico, Indonesia, Brazil and Thailandare the major competitors of India in HS 08 product category. Originality/value: The paper expands the existing agricultural trade literature in three ways. First, it is one of the very few studies that have analyzed RCA for Indian fresh and processed fruits and vegetables using three different types of indices, namely, Balassa's RCA, RSCA and NRCA. Second, the authors provide a number of comparisons related to RCA for Indian fruits and vegetables with other top food exporters in the world for a period of 10 years (2010-20). Third, the authors contribute to agricultural trade literature by assessing the substitutability or complementarity of India in the export of fruits and vegetables with other competing nations by using Spearman rank correlation coefficients. [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 relationship between farm size and productivity has been a topic of interest in agricultural research for decades due to the significance of agriculture in rural economies and its potential to reduce poverty and promote inclusive growth. The relationship between farm size and productivity is influenced by factors such as the type of crop being produced, costs of cultivation, farm management practices, access to inputs and markets and socio-economic conditions. This paper aims to investigate the relationship between farm size and productivity in the context of farming households, their cost of cultivation and the types of crops they produce. Using the Cobb–Douglas production function, the present study estimates the regression function for principal crops such as cotton and paddy in the study area. The findings reveal strong evidence of an inverse relationship between farm size and productivity, indicating that small and marginal farmers are more productive in wetland cultivation (paddy). In contrast, medium and large farmers are more productive in dry land cultivation (cotton). The paper also investigates the availability and accessibility of credit facilities for different farm sizes. It concludes that small and marginal farmers depend mainly on non-institutional credit agencies compared to medium and large farmers. [ABSTRACT FROM AUTHOR]

In the quest for a sustainable farming system around the world, organic farming has emerged as the most promising alternative system that can offer solutions to the growing sustainability issues associated with the current input-intensive agriculture system. Organic farming is also becoming increasingly important due to a significant rise in the demand for organic food across the world. Realizing the importance of organic agriculture in the current Indian agricultural scenario, the Government of India has taken several initiatives to promote organic farming across the country. As a result, there has been a considerable increase in different dimensions of organic farming especially in India over the last decade. However, studies have found that organic farming practices in India are more suited to a few specific regions (such as rain-fed zones, low-productivity areas, hills, and arid zones) as compared to the other parts of the country. In this backdrop, this paper attempts to evaluate the state-wise progress of organic farming in India. To measure the inter-state variation in the performance of organic farming, a composite index has been constructed by taking into account four different performance dimensions viz. area covered under organic cultivation, volume of organic production, volume of organic export, and number of producers practicing organic farming. The result suggests a wide disparity in performances in respect of organic farming among the states in India. Interestingly, the states that have performed well in organic farming are found to have a higher level of organic carbon stock in their soil. This shows the influence of agroclimatic conditions on the organic cultivation practices as popularly postulated in the literature. [ABSTRACT FROM AUTHOR]

Based on an empirical exercise carried out in five villages of Odisha in eastern India, the paper looks into ageing of the farm population and the experiences and responses of farmers of various age groups to farming. The findings of the study indicate that agriculture is greying, farmers are getting older and the youth, particularly of higher and cultivating castes, are averse to farming. The unwillingness of these youths to join farming is mainly attributed to loss of social status, declining profitability in agriculture and discouragement of immediate 'mentors', the middle‐aged farmers, caused by the perpetual decline of farm income and loss of social recognition. The hitherto nonfarming youths, belonging to scheduled castes, scheduled tribes and service‐rendering castes, especially the female youths, are joining farming to fill this gap, mostly as leased‐in cultivators. [ABSTRACT FROM AUTHOR]

A scientometric analysis was conducted to study the productivity and trend dynamics of organic farming research in India covering a period of 20 years (2002-21). The present study aims at science mapping to summarize research activity and its essential aspects. The scientometric analysis outlines the year-wise research growth pattern, prolific authors and their productivity, major contributing journals, major contributing institutions and countries along with keyword occurrences for trend dynamics of organic farming research in India. The data have been sourced from the Web of Science Core Collection database. The most productive period was 2017-21 (44.76% of the total papers published). The growth pattern showed a gradual increase in research during the two decades. Research articles were the most common document type. About 20.11% of international co-authorship was observed in organic farming research. ICAR-based Indian Journal of Agricultural Science was the major contributing source to organic farming research. This study has the potential to benchmark organic farming research in India and play a significant catalytic role in further research in the field of organic farming. [ABSTRACT FROM AUTHOR]

Declining per capita land, water and other resources triggered by changing climate and increasing population need resource conservation technologies (RCTs) for agricultural sustainability. This paper has presented findings from recent research on resource conservation technologies that have been attempted in different regions of India. The superiority of resource conservation technologies (RCTs) such as laser land leveling, zero tillage, bed planting, direct seeded rice, leaf colour chart, green seeker and crop diversification over conventional methods of cultivation practices is evaluated in terms of saving of energy, water and labour, increase in crop yield, water productivity, nutrient-use efficiency, net returns and making agriculture climate smart. Zero tillage with residue retention increase soil organic carbon sequestration, conserve soil moisture and lower soil temperature thus making agriculture climate smart. Direct seeded rice is solution for labour scarcity, excessive water and fuel use in transplanted rice. Use of leaf colour chart saves nitrogenous fertilizers. Laser land leveling and bed planting practices increase water productivity and crop yield over conventional practices. But the machinery required for adoption of RCTs are heavy and costly, so the study has suggested the use of machines on co-operative basis and also the wider scale testing of new technologies under diverse production systems. [ABSTRACT FROM AUTHOR]

Green and decent work in the Global South is inextricably linked to sustaining rural livelihoods especially in agriculture that has undergone significant deskilling under the top-down, technocentric assemblages of the Green Revolution. Additionally, agrarian communities are also seeing youth quitting farming occupations in search of better livelihood options. Scholarly attention to green transitions though has been largely limited to the ecological dimensions. Enacting futures with a focus on ecologically responsible livelihoods need to go beyond existing narratives of technocentric and economic change and foreground the diverse micro institutional innovations that offer newer framings of reskilling. The growing evidence of agroecological initiatives across India indicates less discussed stories of transformation and innovations. Recognising the processes and linkages that allow for, and hinder, transformations at multiple scales and organisational levels is crucial for designing transformative initiatives and policies. Using two illustrative case studies, this paper explores opportunities for green work and the newer skills that might be required to enable sustainable agri-food systems. The case of Natural Farming Fellows (NFFs), a unique programme to encourage young agri-graduates to pursue Natural Farming is presented to understand enabling processes at the grassroots level. The second study explores institutional initiatives to engage rural youth through discussing the pedagogy and curricular approach of a Gandhian university along with opportunities to intern with field organisations. Together, these cases illustrate possible pathways and complexities underlying the process of nurturing sustainable livelihoods, the conception of which needs a broader idea of skilling based on personal aspirations and institutional support. [ABSTRACT FROM AUTHOR]

In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing "crop management", "soil management", and "water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management. [ABSTRACT FROM AUTHOR]

Background: Blackgram is an important pulse crop. The productivity of crops can be enhanced through various input management and selection of suitable genotypes for different systems and seasons of cultivation. An attempt was made to gather published information on crop management for yield maximisation. Methods: This work was done at Department of Agronomy, Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai, Tamil Nadu, India. A systematic cum integrative review of research work done in different parts of World, particularly in India was comprehended. The literature search was done during September, 2019-February, 2020. About 200 review and research papers were screened from various data bases like TNAU e library, ARCC journals, google scholar, research gate and scopus and 88 papers were used to write this paper. Result: This review article documents information gathered on importance of pulses, production and productivity status of blackgram, zero-budget, low-budget management techniques, INM, IPM, weed management besides foliar nutrition for yield enhancement under different systems of cultivation along-with genotype details for different seasons and situations and was presented comprehensively. [ABSTRACT FROM AUTHOR]

India has been one of the largest producers of oilseeds in the world. Correspondingly, the oilseed sector occupies an important position in the agricultural economy of the country. Oilseeds, especially edible oilseeds, have been an integral part of the cropping system in India till the beginning of monoculture triggered by the implementation of a new agriculture strategy since 1960s. This paper evaluates the cropping pattern of oilseed diversity in the Thar Desert of Rajasthan at district level. This paper also highlights the development programmes launched by state and central governments to promote oilseeds production in dry region of the state. Many statistical techniques are used, for instance, Herfindahl Index, Entropy Index, and Composite Entropy Index, to examine the diversification and specialization in oilseeds. [ABSTRACT FROM AUTHOR]

Lessons learnt from past experiences push for an alternate way of crop production. One such is conservation agriculture (CA), which can be seen as a new way forward for conserving resources and enhancing productivity to achieve goals of sustainable agriculture. In recent past, progressive and significant efforts are made to develop, refine and disseminate conservation-based agricultural technologies across globe. Tremendous efforts on zero-till practices and surface management with crop residues for crop production were reviewed. This work was done at Department of Agronomy, Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai-625 104, Tamil Nadu, India. A systematic cum integrative review of research work done in different parts of World, particularly in India was comprehended. Literature search was done during 2020. About 200 review and research papers were screened from various databases like TNAU e-library, ARCC journals, google scholar, research gate and scopus and 84 papers were used. This review article comprehensively documents major principles of CA along with extent and spread of CA across the globe. It also highlights CA in Indian perspective. Various merits and demerits were also discussed with literature support. This review paper discusses rising concerns of conventional agriculture systems with regard to its adoption, positives and negatives of conservation agriculture in promoting conservation agriculture in tropics. [ABSTRACT FROM AUTHOR]

The SCATSAT-1 (Scatterometer Satellite) was launched by ISRO (Indian Space Research Organisation) on September 26, 2016 from the Satish Dhawan Space Centre, Sriharikota, India. With nearly five years of its journey, the Ku-band (13.5 GHz) based SCATSAT-1 made a profound impact on many scientific domains such as ocean-atmosphere dynamics, soil moisture and vegetation dynamics, climate change, hydrology and polar sea-ice and snowmelt studies. As a successor of the Oceansat-2 Scatterometer (OSCAT), the SCATSAT-1 supports the long-term analysis in climate studies, crop yield prediction, and forecasting analysis. In addition, the SCATSAT-1 provides the four different levels of data products at an enhanced resolution to improve the scope of the scatterometer in different applications. Recently the SCATSAT-1 has been explored in many emerging applications apart from oceanography e.g., crop growth, snow cover analysis, jute crop detection and river level estimation with advanced algorithms i.e., machine learning-based classification, information fusion, and super-resolution mapping. Therefore, it is desired to summarise all operational SCATSAT-1 products, applications, and their emerging trends at the global level in the various scientific domains. This paper has summarized the progress made by SCATSAT-1 in different scientific domains since its launch. A meta-analysis has also been conducted in this paper (using the SCOPUS database) to analyse the current research status of SCATSAT-1 in terms of study area targets. This study highlights the features, challenges, and future directions for the scatterometer improvements. • To summarise the historic & current scatterometer missions for earth exploration. • To know the physics & chemistry of earth using data products of SCATSAT-1. • To explore the applications in cryosphere, oceanography, agriculture & hydrology. • To analyse the current status through meta-analysis of published articles. • To summarise the challenges & future directions for the scatterometer improvements. [ABSTRACT FROM AUTHOR]

Industrial revolution and fast deforestation in 20th century pushes the earth to face climate change. Climate change became one of the most important global environmental challenges of the 21st century. Besides other weather abnormalities it is potential threat to world food security as agriculture production is directly dependent on various weather parameters. Even a small alteration in climate, adversely affects agriculture by decreasing the production. Global temperatures have already increased by 0.70°C over the past century and prediction of more rise of temperature is there. Possible changes in temperature, precipitation and CO2 concentration are expected to significantly impact crop growth. India could experience a 40% decline in agricultural productivity by the 2080s. Various food grain crops are going to affect badly under the temperature rise situation. It has been projected that under the scenario of a 2.50°C to 4.90°C temperature rise, rice yields will drop by 32% to 40% and wheat yields by 41% to 52%. This paper summarized study related to climate change projections and its impact on production of various crops. [ABSTRACT FROM AUTHOR]

From the early Holocene onward, the Indian Subcontinent has accommodated a range of diverse human cultures and associated ecological adaptations and lifestyles. Around 10 kyrs ago, the Subcontinent has witnessed the development of later Mesolithic hunter-gatherers and their subsequent regional transitions to pastoralist (Neolithic) and agricultural (Chalcolithic) lifeways. The Holocene climate records reveal discrepancies in the timing and duration of climatic events, which can be attributed to a vast geographic isolation, the influence of height, elevation, and local climatic conditions. These changing climatic patterns including the development of a geographically variable monsoon directly impacted these various cultures including the Harappans and their contemporaries as well as younger Historical and Medieval empires across India, at various levels. In some regions, environmental changes led to uneven cultural transitions, geographic migrations, and the development of regionally-distinct material cultures along with establishment of sedentary life-ways. This paper attempts to present a review broadly correlating general climatic patterns throughout the Holocene period of India with regional cultural dynamics. All geomorphic-climatic zones of the Subcontinent showed strong inter-proxy coherence between 9 and 5 kyrs in response to increased precipitation. After this warming period ends, we see a moderate dry period as a result of a weakening monsoon and an overall tendency toward aridity throughout all zones (after 4 kyrs). The temporal variation of human habitation and respective adaptive responses suggest broad linkages to the varying climatic and physiographic features at a regional scale. Learning how this shaped human eco-dynamics in the past can help us expand our understanding of human history and implement lessons for the present as well as the future. [ABSTRACT FROM AUTHOR]

This paper attempts to identify the determinants of access to the KCC program and empirically evaluate its impact on farmers' use of agricultural inputs and farm household incomes in eastern India. We have also examined whether the possession of KCC reduces the farmer's dependence for borrowing on moneylenders. The estimation is based on Coarsened Exact Matching (CEM) approach, which is an increasingly popular method of estimating causal effects. The study uses a large survey of rural/farming households in eastern India (Bihar, Eastern Uttar Pradesh, Jharkhand, Odisha, and West Bengal). Findings reveal that access to KCC is strongly associated with the socioeconomic and demographic characteristic of farming households. We find that access to KCC increases farmers' use of the agricultural inputs and households and farm income especially for marginal and small farmers. Finally, access to KCC reduces farmer's dependency on moneylenders for borrowing credits by 25 percent. [ABSTRACT FROM AUTHOR]

Grape farms face many climate risks during cultivation, and regulatory and sustainability-related challenges after the harvest. To support the farmers, Government of India (GoI) aims to promote adaptive capacity throughout the grape value chain by improving resilient infrastructure. This paper proposes a novel Grapevoltaics farm design based on shared circular business model principles where resources like energy, water, and land are shared between grape farmer and solar plant operator. The resilience (in the near term) and sustainability (in the long term) of such multifunctional systems may be influenced by the decisions taken by the involved stakeholders in terms of resource sharing and support extension when facing climate risk events. To address stakeholder engagement, the proposed Grapevoltaic farms leverage blockchain smart contract, making them Blockchain-enabled Grapevoltaics (BCeDGVs). To understand the feasibility, 1-ha Grapevoltaic farm is modeled for 12 grape growing regions accross India and analysed using Resilience Performance, Life Cycle Analysis, and Techno-Economic (RePLiCATE) approach. The results show that the solar panels provide resilience support based on the rules of engagement written on blockchain smart contracts. The BCeDGVs has 1295.28–2908.08 kWh/kWp/day electricity generation potential across India with a lifetime average of 15,380.65 MWh even after considering the national average photovoltaic degradation rate of 1.9%. The observed grape yields vary between 2 and 31.25 Mt/ha with an average yield of 45 Mt/ha (table grape) and 40 Mt/ha (wine grape), and the rainwater harvest varies between 1.23 to 7.32 M l. The economic assessment revealed that grape farmers and photovoltaic plant operators across India under BCeDGVs could have revenues ranging between INR 56.25 to 225 million and INR 29.48–66.19 million, respectively, suggesting to relook into the feed-in tariffs and crop selling prices. The environmental sustainability results suggest that 1.984 ha/MW land-use mitigation and 12–100% reduction in groundwater dependency are possible while maintaining the minimum and maximum national average global warming potentials as 0.074 and 0.088 kg CO 2 -eq./kg grape, respectively. Overall, the integrated assessments find that the BCeDGVs may promote low carbon, climate resilient grape cultivation in India and has possible integration with GoI's GrapeNet platform. This study highlights a new research direction symbiotic grape farms and energy networks. [Display omitted] • Grapevoltaics co-locate Vitis vinifera cultivation with solar photovoltaics. • Blockchain smart contract facilitated shared circular business model in agriculture. • Harvested rainwater cut emissions for grape crop and solar panel cleaning. • Dynamic operation of solar panels protects the grape crop from rain events. • Grapevoltaics integration with APEDA's GrapeNET platform, Govt. of India. [ABSTRACT FROM AUTHOR]

Different diseases are observed in vegetables, fruits, cereals, and commercial crops by farmers and agricultural experts. Nonetheless, this evaluation process is time-consuming, and initial symptoms are primarily visible at microscopic levels, limiting the possibility of an accurate diagnosis. This paper proposes an innovative method for identifying and classifying infected brinjal leaves using Deep Convolutional Neural Networks (DCNN) and Radial Basis Feed Forward Neural Networks (RBFNN). We collected 1100 images of brinjal leaf disease that were caused by five different species (Pseudomonas solanacearum, Cercospora solani, Alternaria melongenea, Pythium aphanidermatum, and Tobacco Mosaic Virus) and 400 images of healthy leaves from India's agricultural form. First, the original plant leaf is preprocessed by a Gaussian filter to reduce the noise and improve the quality of the image through image enhancement. A segmentation method based on expectation and maximization (EM) is then utilized to segment the leaf's-diseased regions. Next, the discrete Shearlet transform is used to extract the main features of the images such as texture, color, and structure, which are then merged to produce vectors. Lastly, DCNN and RBFNN are used to classify brinjal leaves based on their disease types. The DCNN achieved a mean accuracy of 93.30% (with fusion) and 76.70% (without fusion) compared to the RBFNN (82%—without fusion, 87%—with fusion) in classifying leaf diseases. [ABSTRACT FROM AUTHOR]

This paper explores the prevalence of child labour and long working hours in India using 2019 data, with estimates for boys and girls that deal with age-related child development concerns related to long hours of work. We use international suggestions to define harmful child labour from ILO and UNICEF and a nationally defined time-threshold model in analysing the child-labour phenomenon. Measuring time by the three measurement systems and splitting children by age, gender, and cultural components make harmful forms of labour become clearer. The results show that girls doing agricultural labour and boys working as non-agricultural labourers had the longest average working hours in India. Important social-group differentials emerge. This study implies that policy-makers can be, and need to be, aware of explicit measures of hours worked by children aged six to 17. [ABSTRACT FROM AUTHOR]

Aim/Purpose This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendations for Researchers Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the dataset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India. [ABSTRACT FROM AUTHOR]

One of the most fertile areas of modern cultural research is the study of Rituals performed by indigenous communities. Rituals maintain and preserve the cultural identity of a society, especially, in preliterate oral tribal societies of the world. In such communities, Agriculture and Religion are intrinsically linked to the socio-religious sphere of their life. Their survival is solely based on human-nature dynamics. Such communities practice nature-worshipping and believe in the interconnectivity of the physical and supernatural worlds. Hence, they perform many rituals to appease the malevolent and to express gratitude to benevolent supernatural beings, deities, and spirits. Tangams are one such animist community that is the least populated and lesser-known ethnolinguistic group of Arunachal Pradesh. Their everyday life is based on the performance of several rites and rituals. These ritual performances are part of their cultural heritage. However, such practices are slowly in decline due to various internal and external factors. Therefore, this paper is a descriptive attempt to document, elucidate, preserve, and disseminate the dying Maye ritual practice and its associated belief systems. [ABSTRACT FROM AUTHOR]

Domesticated rice (Oryza sativa), typically a water-intensive crop, is widely cultivated in the semi-arid zones of South India and Sri Lanka and is a staple among sedentary populations in these regions when the opposite should prevail in light of environmental constraints. This paper investigates the origins of large-scale rice cultivation in South India and Sri Lanka and the attendant innovations in hydraulic technologies using a wide range of sources but with a special focus on Tamil "Caṅkam" texts, the earliest surviving textual corpus for the Dravidian language family. [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 investigates the livelihood vulnerability experienced by agricultural households in Kuttanad, a below sea level farming system in southern India, in the aftermath of a major flood in August 2018. For this purpose, we constructed a flood coping strategy index (FCSI), to measure coping strategy intensity, using the data on the severity and frequency of various coping strategies adopted by households. Furthermore, we estimated a Tobit regression model to identify the factors influencing the intensity of coping strategy choices. The FCSI revealed that only two per cent of agricultural households experienced a 'severe' level of vulnerability because of the quick and effective policy response of the Kerala state government. In addition, Tobit regression analysis indicated that female‐headed and labour households are more vulnerable than their respective counterparts. While income exerts a negative influence on the degree of livelihood vulnerability, agricultural landholding has a positive effect, as it increases cultivation loss during a flood. [ABSTRACT FROM AUTHOR]

Traditional festivals are the amalgamation of various aspects of the Intangible Cultural Heritage which reflects the worldview and culture of a particular community. Understanding festivals in the natural socio-cultural context is very important to understand the culture of a particular community. This paper attempts to describe the Ronghoan festival of the Nocte tribe inhabiting in Tirap district of Arunachal Pradesh, India and discusses various aspects of the festival by interpreting the associated rituals and symbolic attributes, which largely manifest their worldviews. It also highlights the multifaceted nature and role of festival in Nocte society and tries to identify a variety of tasks performed by festival. Ronghoan is a harvest festival of millet celebrated by Nocte community of Khonsa (Hakhun) village during the month of June-July every year. It is celebrated to propitiate the god, Almighty Rang for his blessing in order to attain good crops and well-being of the village. [ABSTRACT FROM AUTHOR]

Public investment through its long term benefits is one of the significant contributors to agricultural growth in India. Despite the fact, the public sector investment in Indian agriculture declined over the years and the researchers have considered increasing input subsidy as the main cause of its decline. This paper tries to empirically examine the claim that input subsidy discourages public investment in Indian agriculture. Applying an "auto‐regressive distributive lag" (ARDL) model to time‐series data at the national level and panel‐ARDL model to sub‐national level panel data the study confirms that input subsidies do negatively affect public investment in Indian agriculture and results in discouraging the public sector investment. Therefore, the policy implication of the study suggests that for more efficient and sustainable growth in Indian agriculture, the need of the hour is the careful analysis of all these input subsidies and gradual removal of unproductive ones so as to make resources available for farm investment in social and physical infrastructure. This may help to accelerate and sustain the growth dynamics of Indian agriculture. [ABSTRACT FROM AUTHOR]

Agriculture is the source of GDP for any country. Effort is being made to analyze the trend of contributors at India and global with identification of major/top research areas, publishing journals, fund sources, organizations, researchers; group authors and cited papers in the index databases of Clarivate Analytics from 2014-2019. India is leading globally in the area of smart agricultural, more paper published in the stream of computer science and engineering. After exploration of this area it is observed that India maintains its growth at global level from last six years. Pure research work performed mainly in environmental science, S & T and biodiversity conservation. National agencies are funding more at national and International level. French National Centre for Scientific Research (CNRS) and Indian Council of Agricultural Research (ICAR) are doing much effort to promote smart agriculture at abroad and home respectively. IEEE (Institute of Electrical and Electronics Engineers) and IOP (Institute of Physic) are top group authors. Our study reveals that Indian papers did not get much citation. [ABSTRACT FROM AUTHOR]

The use of ergonomics principles can be traced back to ancient times, much before the coining of the term 'ergonomics'. Ancient civilisations of Greece, China, and India followed well laid down principles of usability and human factors. These are evident in their furniture design, tool design, and other practices to minimise workers' workload. In ancient India, most daily activities like cooking, dining, defecation, etc., and occupations like pottery making, shoemaking, education in gurukuls (traditional residential schools), etc., used one or the other asanas (postures). The aim was to maintain the body's natural shape, avoiding the ill effects of poor body posture. The present paper reviews those domains of practice viz. posture, architecture, agriculture and furniture design in ancient India (2500 BCE to 1100 CE), where modern ergonomics principles seem evident. [ABSTRACT FROM AUTHOR]

Tobacco use is associated with an annual global economic cost of two trillion dollars and mortality of half of its regular users. Tobacco leaf cultivation is the starting point of the tobacco cycle. Tobacco farming employs millions of small-scale tobacco farmers around the globe, most of whom are out growers who rely on the tobacco industry. This paper aims to map the regions of greatest tobacco production globally (i.e., the US, Brazil, China, Indonesia, India, and Zambia) and tobacco use rates in these locations. Smoking rates were higher in those areas, except for India, where important population subgroups reported an upward trend for tobacco smoking. In general, there was a relationship between tobacco farming and tobacco smoking. Tobacco farming may lead to a higher risk of tobacco use and lower adherence to tobacco control policies. Therefore, promoting viable alternative livelihoods for tobacco farmers must have dual benefits. Additionally, specific health prevention policies might be necessary for those populations reporting higher tobacco use and lower perception of tobacco-related health risks. [ABSTRACT FROM AUTHOR]

Agriculture plays a central role in the economic development of a country. The population of India is expected to reach 1.5 billion by 2050 with a current decadal growth rate of ~18%. The review paper is written to highlight the current and future challenges facing the agriculture sector in India. There are various challenges that affect the Indian economy such as food security, soil quality deterioration, water scarcity, lack of proper food storage structures, land degradation etc. Recent pandemic situation has affected the agricultural sectors, will lends to the major changes in the future, which needs to be discussed and planned to meet the future requirements. [ABSTRACT FROM AUTHOR]