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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]

This paper revisits the debate on the relationship between farm size and utilisation of agricultural land. Since the 1960s, a large number of studies have been examining this relationship and suggesting possible explanations supporting their thoughts. Three significant hypotheses exist in the literature forwarded by different scholars in this regard: an inverse relationship, a positive and a U-shaped one. Using the recent data from the Agriculture Census of India, this paper attempts to analyse the spatio-temporal trends of the relationship between farm size and land utilisation and examine the hypotheses and explanations already advanced, taking a state-level analysis in India. We found an inverse relationship where smaller farms are more fully and intensively used compared to the larger farms; however, the increasing share of unutilised land among the small and medium farms in the recent decade of 2005–2006 to 2015–2016 indicates a losing ground of small farmers because of ongoing agrarian distress. [ABSTRACT FROM AUTHOR]

Background: Protecting crops from various threats, including vermin, pests, animals and rodents, is essential for sustaining agriculture. In India, especially in regions like Rajasthan, animal attacks result in significant crop losses, up to 40%. This section underscores the importance of safeguarding crops against these threats. Methods: According to our sustainable agriculture assessment and cognition of the primary issue of the community that was conducted across multiple rural sites within the Sawai Madhupur district of Rajasthan from 2020 to 2023. This paper employs a comparative method to conduct a comprehensive review of animal-repellent systems, along with delving into interdisciplinary research in related fields to identify key factors influencing their effectiveness and proposes a novel holistic farm protection solution system. Result: The proposed system offers robust crop protection while aiding environmental conservation and preserving local wildlife in Rajasthan, India. This paper underscores the importance of adopting IoT-based technologies for sustainable agriculture. It is a valuable resource for researchers, agronomists and various agriculture stakeholders to implement these advancements. This system holds promise for enhancing crop security, minimizing resource waste and ensuring food chain stability for generations to come. [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]

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 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]

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]

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]

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]

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]

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]