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Uncertainty and uneven distribution of monsoonal rainfall and its consequences on crop production is a matter of serious concern in India, specifically, in the Indo-Gangetic plain region. In this study, drought patterns were investigated through standardised precipitation index (SPI) of varying timescales, using the India Meteorological Department (IMD) precipitation data (1901-2021). We analysed the spatio-temporal pattern of different drought characteristics (frequency, duration, severity, intensity) of the Indian Gangetic basin using run theory. The bivariate copula method has been incorporated to combine two drought properties (severity and duration). Copula integrates multivariate distribution and considers the dependency rate among the variables. The five most widely used copulas from various copula families, elliptical (normal, t-copula) and Archimedean (Clayton, Gumbel, Frank), were estimated for modelling, and the best fit copula was selected. The study revealed that seasonal drought is more frequent and intense in the Upper and Middle Gangetic Plain, whereas annual drought is quite scattered in nature. It is worthy to mention that downward drought trends were observed in this agricultural belts significantly after 1965; specifically, in the Upper, Middle, and Trans Gangetic Plain regions. With increasing drought duration and severity, the drought return period raised, but the frequency decreased gradually. Most of the droughts characterised by less duration and severity occurred with a return period below 10 years for the whole region. The major 100 + years return period droughts were to be found after 1960 and their frequencies were significantly higher after 2000. The most recent remarkable droughts with more than 100 years of return occurred during 2008-2011 and 2016-2018 in the Upper and Middle Gangetic plains, whereas in the Lower Gangetic plain, a hundred-year return period drought was occurred during 2010-2013. This study provides agroclimatic-zones-wise significant information of drought characteristics and its nature of occurrence in the Indian Ganga Basin. The results enhance the understanding of drought management and formulation of adaptive strategies to mitigate the adverse impact of droughts., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study was carried out to understand the pattern of surface energy fluxes over a periodical scale and energy balance closure using Large Aperture Scintillometer and Micrometeorological tower. The standalone technique as 'Scintillometry' which observes the structure parameter of refractive index based on Monin-Obukhov Similarity theory, has the potential to measure the sensible heat flux precisely. This paper discusses the surface energy balance components and energy balance closure over a period of August 2017 to June 2018. The maximum mean energy fluxes Rn, G, H and LE were observed in September (98.6 Wm-2), May (13.9 Wm-2), June (53.3 Wm-2) and August (82.1 Wm-2), respectively. The overall mean ET was observed at the rate of 1.36 mm day-1 during the study period. This scintillometry technique may further use in evapotranspiration modelling from polar orbiting satellite to geostationary satellite over a heterogeneous and undulated landscape. [ABSTRACT FROM AUTHOR]

The average annual income of farmers in India is well below the poverty line, primarily due to off-season agriculture and a lack of awareness about optimal farming techniques and crop management. This research paper presents an AI-based web application designed to address these challenges and improve farmers' financial and mental well-being. Our solution, comprised of a farming tools leverage system and a collaborative farming forum, empowers farmers with the knowledge and tools needed to make informed decisions and increase their income. The system leverages MongoDB, asynchronous calling, and Fast API for efficient data management and real-time interactions. AI/ML services assist with crop recommendations, crop disease detection, and price predictions. Load balancing ensures optimal performance, and Pusher JS enables real-time communication. Billing services and a dashboard provide income insights, while geographic data enhances machine learning recommendations. In conclusion, this research contributes to alleviating poverty and enhancing the livelihoods of farmers in India by providing a comprehensive solution to the challenges they face. [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]

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]

Efficient sugarcane production is crucial in today’s competitive agricultural landscape. Traditional planting methods are cost-intensive and time-consuming and often fail to achieve adequate compression of buds due to the stalk planting nature of sugarcane. To address these challenges, a novel approach using machine vision to identify and plant sugarcane nodes is proposed. Sugarcane holds significant economic importance in India and various other countries, making its cultivation a cornerstone of many economies. The development of a sugarcane node cutting machine aims to revolutionize the process of preparing sugarcane seedlings. This machine streamlines the time-consuming task of cutting nodes, thereby reducing waste and effort associated with traditional methods. The paper focuses on designing and testing a machine for efficiently cutting sugarcane buds, intending to mechanize the preparation stage of sugarcane seedlings. By cutting undamaged bud-containing sugarcane stalks, this machine eliminates the need for laborious manual cutting, providing a timesaving and cost-effective solution for farmers. Furthermore, by measuring cutting force and power requirements, the research aimed to optimize the efficiency of sugarcane cutting processes. [ABSTRACT FROM AUTHOR]

Agriculture is a strong foundation of economy for many agriculture-based countries like India. In the economies of many nations, including India, where banana farming is quite important, agriculture is a key component. Musa Acuminata is the scientific name for the banana, and a banana tree normally lives for about 25 years. Banana cultivation typically lasts 9 to 12 months. The persistent threat of illnesses, which can drastically affect yield and quality, is just one of the many difficulties faced by the banana industry. This study focuses on overcoming these obstacles by observing the health of the soil and detecting infections in banana plants. The yield and crop quality of bananas can be negatively impacted by a number of variables, including illnesses. Developing an efficient method for spotting diseases in banana plants is our main goal, and we're also constantly keeping an eye on the condition of the soil. The main methodology we use for identifying diseases is SVM. In addition, we use procedures for soil monitoring to measure soil pH levels, minerals, and other essential elements that affect the health of the soil. The study shows good results in identifying diseases, ensuring prompt treatments to reduce risks associated with diseases. Furthermore, strong plant growth and increased yields are facilitated by realtime soil health monitoring. By tackling the ongoing threat of illnesses in banana cultivation through SVM-based disease identification and ongoing soil health monitoring, this research offers a vital contribution to the agricultural sector. The study shows promise in identifying diseases and guaranteeing prompt actions to reduce risks associated with diseases. Furthermore, strong plant growth and increased yields are facilitated by real-time soil health monitoring. This paper is focused on both types of diseases of banana plant and minerals in the soil. [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]

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]

Purpose: The present study provides evidence on export advantages of horticultural commodities based on competitiveness, trade balance and seasonality dimensions. Design/methodology/approach: The study delineated horticultural commodities in terms of comparative advantage, examined temporal shifts in export advantages (mapping) and estimated seasonality. Product mapping was carried out using the Revealed Symmetric Comparative Advantage (RSCA) and Trade Balance Index (TBI). Seasonal advantages were examined through a graphical approach along with the objective tests, namely, modified QS-test (QS), Friedman-test (FT) and using a seasonal dummy. Findings: Cucumbers/gherkins, onions, preserved vegetables, fresh grapes, shelled cashew nuts, guavas, mangoes, and spices emerged as the most favorable horticultural products. India has a strong seasonal advantage in dried onions, cucumber/gherkins, shelled cashew nut, dried capsicum, coriander, cumin, and turmeric. The untapped potential in horticulture can be addressed by handling the trade barriers effectively, particularly the sanitary and phytosanitary issues, affecting the exports. Proper policies must be enacted to facilitate the investment in advanced agricultural technologies and logistics to ensure the desired quality and cost effectiveness. Research limitations/implications: Commodity-specific studies on value chain analysis would provide valuable insights into the issues hindering exports and realizing the untapped export potential. Originality/value: There is no holistic and recent study illustrating the horticulture export advantages covering a large number of commodities in the Indian context. The study would be helpful to the stakeholders for drawing useful policy implications. [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 rural areas with higher agricultural energy consumption, ensuring low-carbon transformation and rapid penetration is crucial; therefore, the importance of rural energy system in energy transformation is even more prominent. In order to better understand the research progress of rural integrated energy system, the existing structure of rural energy system and design method are briefly introduced, and the bibliometric method is applied to analyze publications from 2013 to 2023. Based on the Scopus database, 915 publications have been retrieved. These publications are analyzed from the perspective of citation, author, address, and published journal. It is concluded that existing researches mostly use conventional energy technologies to achieve power supply in rural areas, lacking analysis of the potential application of emerging energy technologies and research on multi-energy demand. Furthermore, the review reveals the economy of grid-connected rural energy system is mainly related to geographical location, system configuration, and resource endowment. The bibliometric analysis indicated that these publications are mainly from India and China; the average citation is 24.98, and each article is co-published by 3.66 authors, 2.26 institutes, and 1.46 countries. This work is helpful for scholars to understand the research status on the rural integrated energy system. [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]

Purpose: Warehouse receipt-based financing (WRF), an innovative instrument with its structure embedded in the agricultural value chain can potentially address farmers' concerns about timely credit access and accessible remunerative markets. However, studies indicate farmers' exclusion from currently practiced WRF mechanisms across developing countries. Transaction cost and lack of assured remunerative markets post storage are the challenges thwarting farmers' participation. The study explores how these challenges can be addressed by analyzing a case study. The finding will help in coming up with a farmer-inclusive WRF mechanism. Design/methodology/approach: The study uses a case study as an analysis tool. Primary data is gathered through farmers. Descriptive statistics and partial least squares (PLS) approach to structural equation modeling methodology has been adopted for empirical testing of the hypothesis of the study. The study uses SMART PLS 3.0 for analysis of data. Findings: Single window offering of multiple value chain operations and technological intervention in physical handling substantially reduces transaction costs for farmers. Sustained farmers' participation in the case supports this finding. The presence of an assured market (PAM) is found to have a positive and significant relationship with WRF in the case of beneficiary farmers. The PAM is found to have a negative yet significant relationship with WRF in the case of nonbeneficiary farmers. Critical success factors of the entity KisanMitra stated in the case substantiates a farmer-inclusive WRF mechanism. Research limitations/implications: The study analyzes a case study of specific geography. However, similarities enlisted across developing countries in the introduction section provide a scope of generalization of findings across developing countries. The identified factors for a farmer-inclusive WRF mechanism will enable the governments, policymakers and development institutions to ascertain and align their WRF implementation measures to inculcate and upgrade these factors to the prospective WRF agents. Future studies can explore the replication of farmer-inclusive WRF mechanisms across other geographies. The studies also explores the role of technological interventions in further reducing the transaction cost and suitable policy modifications to encourage replication of the study in other geopgraphical context. Originality/value: The study on WRF and the methodology adopted is first of its kind to identify factors for a farmer-inclusive WRF mechanism. [ABSTRACT FROM AUTHOR]

Farmer producer companies (FPCs) have widely transformed the livelihoods of the people and mainly squeezed the income and living standard conditions in the rural sectors, specifically a primary sector engaged majorly with village economies. This article diagnoses the collective cooperative concept and formation of FPCs in the country since their establishment in the year 2008–2021. We utilized secondary data from the Ministry of Corporate Affairs (MCA) and the outcome of the study discusses the state and zone‐wise performance of FPCs in the country. The result found that only a few zones are performing in the FPCs businesses which makes it sustainable. The study further adds the sustainable measures for performance of the FPCs. [ABSTRACT FROM AUTHOR]

Farmers' use of agricultural technology is necessary for reducing poverty. However, women make up most poor smallholder farmers in India and are generally unable to access relevant opportunities. Tandem to supporting women farmers in technology adoption is increasing their empowerment. While women's empowerment has been on policy agendas in India for several years, progress has been slow and results are mixed. Using primary data from West Bengal, this study finds that female farmers with access to agricultural technologies will likely have social advantages over those without, but institutional and social support is necessary to facilitate real empowerment and overall development. [ABSTRACT FROM AUTHOR]

The Indian Flying Fox Pteropus medius plays a major role in the maintenance of a healthy ecosystem and is very specific in selecting roosting sites. The present study was conducted from January 2021 to March 2022 to document the occurrence of roosting colonies, the roosting sites and the population characteristics of P. medius in Kurukshetra district, Haryana. Of the 10 roosting sites identified in four tehsils of Kurukshetra district, 60% were located close to water sources, 20% were near agricultural fields, and the remaining sites were in roadside plantations. Eight roosting sites were observed to be permanent and two were temporary. A total of 233 trees belonging to seven families, eight genera, and nine species were identified as roost trees for this species. Our survey results show that P. medius preferred (77.42%) roosting majorly on trees of the Myrtaceae family (Eucalyptus), while very few individuals (0.5%) were recorded on Phoenix dactylifera. A positive Pearson correlation between the population of the roosting bats and the roost tree characteristics such as height (r = 0.320, p <0.001), dbh (r = 0.226, p <0.001), and circumference (r = 0.293, p <0.001) was also observed. The findings of the current study revealed that P. medius prefers to roost on tall trees with large diameters at breast height, located near water bodies and agricultural fields. Protecting such large and tall trees would benefit the conservation of roosting sites of this species and its populations. [ABSTRACT FROM AUTHOR]

Natural Farming represents an agro-ecological methodology for farming that emphasizes regenerative practices with an aim to promote holistic ecological balance and reduce the dependence on external inputs as well as financial resources. Substantial concern has recently arisen over the need to promote agroecosystems that are more sustainable in order to improve the deteriorating soil health as well as reversing the yield plateau of crop. So, the current on farm field experiment was executed comprising of 8 treatments with different combination of natural farming inputs (Ghanjeevamrit, Jeevamrit, Beejamrit), organic fertilizer (such as FYM), integrated nutrient management (NPK, FYM, Azotobacter and Azolla) and in-organic(NPK) to examine and compare the consequence of natural farming inputs, organic fertilizer and in-organic dosage of fertilizer on soil nitrogen uptake, soil physicochemical properties, soil biological properties, soil microbial population and crop yields in a rice-wheat cropping system over two crop seasons 2021–23 [rice (Pusa-1509) and wheat (HD-3086)]. The study results demonstrated that there was significant (p  <  0.05) increase in the soil’s nitrogen availability and nitrogen uptake with the use of natural farming inputs as compared to control treatment, whereas, natural farming treatments (TNF1, TNF2, TNF3, TMNF) were inferior than integrated nutrient management (TINM) and recommended doses of fertilizer (TRDF) treatment in case of nitrogen uptake by both rice and wheat crop. The soil enzymatic activity (Dehydrogenase, β-glucosidase, and urease), soil microbial biomass carbon and nitrogen, and soil microbial population (Bacteria, fungi, and actinomycetes) were significantly (p  <  0.05) higher in treatment receiving natural farming inputs compare to in-organic fertilizer and organic fertilizer. A positive and significant correlation was observed between potential mineralization nitrogen and soil enzymatic activity (Dehydrogenase, β-glucosidase, and urease), soil microbial biomass carbon and nitrogen and soil microbial population (Bacteria, fungi, and actinomycetes). The crop yield at the end of experiment recorded to be highest in treatment TINM (75% RDF (In-organic)  +  25% RDF (FYM)  +  BGA) i.e., (Rice4.76  t/ha and Wheat- 5.82  t/ha) compared to TRDF and TNF. A crop yield reduction of 14.2% was observed in treatment receiving natural farming inputs compare to TINM. A significant increase in crop yield was observed in TMNF (Jeevamrit (25%)  +  Ghanjeevamrit (25%)  +  50% RDF through FYM  +  Beejamrit) compare to Tc (Control) and TFYM (Farmyard manure). Therefore, our study suggests that adoption of natural farming inputs over time can facilitate the enhancement of soil biological health of Inceptisol of Trans Gangetic Plain of India. [ABSTRACT FROM AUTHOR]

Farming and agriculture are the oldest professions, but they are adapting to the technology revolution to accommodate the world's growing population. UAV technology is part of the agriculture revolution, which aims to boost crop yields, properly monitor fields, and handle manpower shortages and resource efficiency. Rural India's tiny farmers cannot afford UAV technology; therefore, it has not yet spread. Payload capacity, endurance, and selective spraying are other considerations. Thus, a low-cost, long-lasting UAV is necessary. This study modified the arm assembly to create a cheap hexacopter UAV. The endurance increased by 10% when 1.5 kg was lost. ABS plastic was used to make the modular arm. For working loads of 9 kg and 10 kg, pesticide/fertilizer spraying saves time, money, and manpower. Thus, a pressure-area coverage-cone angle connection is needed. This study examined spray patterns at different pressures and heights by varying flat fan nozzle and complete cone nozzle orifice diameters. These factors were linked, helping farmers choose the right nozzle. This nozzle was installed in the UAV and field-tested for paddy crops, showing a significant production improvement and lower operational cost. Chemical use pollutes and leaves traces in produce. Precision farming with artificial intelligence (AI) has solved this problem. In this experiment, AI algorithms were used to lemon leaves. Three AI systems were tested on different datasets to forecast plant stress by analyzing leaves due to technical constraints. CNN's accuracy and computing speed make it ideal for precision farming. This work's UAV was 30% cheaper than commercial UAVs and had more durability. Farmers will also benefit from the flat fan and complete cone nozzles' pressure-area coverage connection. [ABSTRACT FROM AUTHOR]

Habitat loss for food production is a key threat to global biodiversity. Despite the importance of dietary choices on our capacity to mitigate the on-going biodiversity crisis, unlike with specific ingredients or products, consumers have limited information on the biodiversity implications of choosing to eat a certain popular dish. Here we estimated the biodiversity footprints of 151 popular local dishes from around the world when globally and locally produced and after calorical content standardization. We find that specific ingredients (beef, legumes, rice) encroaching on biodiversity hotspots with already very high agricultural pressure (e.g. India) lead to high biodiversity footprint in the dishes. Examples of high-biodiversity-footprint popular dishes were beef dishes such as fraldinha (beef cut dish) originating from Brazil and legume dishes such as chana masala (chickpea curry) from India. Regardless of assuming locally or globally produced, feedlot or pasture livestock production, vegan and vegetarian dishes presented lower biodiversity footprints than dishes containing meat. Our results demonstrate the feasibility of analysing biodiversity footprint at the dish level across multiple countries, making sustainable eating decisions more accessible to consumers. [ABSTRACT FROM AUTHOR]

Background: The cotton cultivation in India is influenced by high inputs and expenditure. But the farmers do not get sufficient access to institutional credit; hence the farmers borrow money at the higher interest from other sources. Higher risks due to the impacts of climate change furtherincrease the economic burden and uncertainty of getting reasonable income to the farmers. Furthermore, the crop suffers due to incidence of pests and diseases. Because of this situation, farmers encounter risks and pressures, yet they seldom openly reveal the psychological strain that accompanies their agricultural work. Hence, the current study was carried to identify strategies to be adopted by cotton growers to manage risk and stress in crop management, agricultural extension and input arrangement. Methods: The study was conducted by collecting information from 240 cotton growers of four blocks in Gajapati and Rayagada districts of Odisha. The opinion on socio-psychological, technological competency and input supply strategies to be adopted was collected individually through a semi-structured schedule and analyzed. Result: The opinion of cotton growing farmers on the strategies to be adopted in minimizing the risks and stresses in cotton production on socio-psychological, technological competency and input supply showed that the farmers preferred to have participatory decision on cluster approach, developing good drainage facility and advance supply of inputs and materials, periodical visits by extension officials' to the farmers' field and create awareness on latest varieties availablewith high yield and training on latest technologies and ensuring quality and timely input supply, providing information on market price and immediate payment for their produce. [ABSTRACT FROM AUTHOR]

Farmers, notably in India, face challenges such as inadequate expertise in crop selection and crop failures due to disease. Deep Learning's untapped potential in agriculture, limited by data quality and processing constraints, presents an opportunity for enhancement. The objective is to give an open-source online application is developed using Flask to aid farmers in informed decision-making. Utilize Precision Agriculture principles to recommend crops based on soil types, features, and data-driven insights to boost productivity. The various methods based on a machine learning-based ensemble model is employed to suggest suitable crops based on soil data, focusing on accuracy and efficiency. The outcome of the developed system shows promise in enhancing input-output efficiency, improving decision-making, and reducing unsuitable crop choices, thereby boosting agricultural productivity and positively impacting India's economy. This approach demonstrates practical potential in resolving farming challenges by leveraging machine learning for crop recommendations, potentially revolutionizing agriculture in India and beyond. [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]

Purpose: This study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches. Design/methodology/approach: The study used a two-stage approach. In the first stage, the efficiency scores of decision-making units' efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model. Findings: The results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency. Research limitations/implications: The study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias. Practical implications: Given the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices. Social implications: Although many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system. Originality/value: No prior study has used a hybrid framework to examine the performance of a chemical-free farming system. [ABSTRACT FROM AUTHOR]

Tomatoes, which are extensively grown in Indian agricultural fields, are among the commonly cultivated vegetable crops. The tropical climate in India provides favorable conditions for tomato cultivation. However, various factors, including climatic conditions and other elements, can adversely affect the normal growth of tomato plants. Plant diseases present a significant risk to crop production and can lead to economic losses, adding to the challenges posed by climate conditions and natural disasters. Unfortunately, traditional methods of detecting diseases in tomato crops have proven to be ineffective and time-consuming. This study primarily aims to accurately identify tomato plant leaf diseases that are found using image analysis. Various methods have been applied for extracting the features that have been used to improve the accuracy of classification. Alex Net, InceptionV3, and a CNN (Conv Net) algorithm have been utilized to classify many types of tomato plant diseases. When comparing the results, it is notable that the CNN (Conv Net) classifier outperforms the other two models. The findings demonstrate the practical applicability of the model in real-life scenarios. [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]

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