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Effective management of water resources is crucial for sustainable development in any region. When considering computer-aided analysis for resource management, geospatial technology, i.e., the use of remote sensing (RS) combined with Geographic Information Systems (GIS) proves to be highly valuable. Geospatial technology is more cost-effective and requires less labor compared to ground-based surveys, making it highly suitable for a wide range of agricultural applications. Effectively utilizing the timely, accurate, and objective data provided by RS technologies presents a crucial challenge in the field of water resource management. Satellite-based RS measurements offer consistent information on agricultural and hydrological conditions across extensive land areas. In this study, we carried out a detailed analysis focused on addressing agricultural water management issues in India through the application of RS and GIS technologies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we systematically reviewed published research articles, providing a comprehensive and detailed analysis. This study aims to explore the use of RS and GIS technologies in crucial agricultural water management practices with the goal of enhancing their effectiveness and efficiency. This study primarily examines the current use of geospatial technology in Indian agricultural water management and sustainability. We revealed that considerable research has primarily used multispectral Landsat series data. Cutting-edge technologies like Sentinel, Unmanned Aerial Vehicles (UAVs), and hyperspectral technology have not been fully investigated for the assessment and monitoring of water resources. Integrating RS and GIS allows for consistent agricultural monitoring, offering valuable recommendations for effective management. [ABSTRACT FROM AUTHOR]

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]

Accurate and quantitative regional estimates of the carbon budget require an integration of eddy covariance (EC) flux-tower observations and remote sensing in ecosystem models. In this study, a simple remote sensing driven light use efficiency (LUE) model was used to estimate the primary productivity for major cropping systems using multi-temporal satellite data over the Saharanpur district in India. The model is based on radiation absorption and its conversion into biomass. The LUE model was implemented for major crop rotations derived from the time-series of Sentinel-2 and Landsat 8 with monthly satellite-based spatially explicit fields of photosynthetically active radiation (PAR), fraction of absorbed PAR (fAPAR) and down-regulated light use efficiency. Incident PAR and fAPAR were estimated on monthly basis from the ground-calibrated empirical equation using INSAT-3D insolation product and remote sensing–based vegetation indices, respectively. Spatial LUE maps created by down-regulating maximum LUE (EC tower-based) with water and temperature stressors derived from land surface water index (LSWI) and EC-based cardinal temperature, respectively. LUE-based modeled GPP over the sugarcane-wheat system was found higher than the rice-wheat system in Saharanpur district. This is because C4 crop (sugarcane) has very high photosynthetic efficiency compared to C3 crops (rice and wheat). Modeled GPP over the sugarcane-wheat system was found in good agreement with observed EC tower-based GPP (Index of Agreement = 0.93). Further regionally calibrated remote sensing–based LUE model well captures gross photosynthesis rates (GPP) over cropland ecosystem compared to globally modeled MODIS GPP product. [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]

In this study, CO2 exchange over sugarcane and wheat growing season was quantified by continuous measurement of CO2 fluxes using eddy covariance (EC) system from January 2014 to June 2015. We also elaborated on the response of CO2 fluxes to environmental variables. The results show that the ecosystem has seasonal and diurnal dynamics of CO2 with a distinctive U-shaped curve in both growing seasons with maximal CO2 absorption reaching up to −8.94 g C m−2 day−1 and −6.08 g C m−2 day−1 over sugarcane and wheat crop, respectively. The ecosystem as a whole acted as a carbon sink during the active growing season while it exhibits a carbon source prior to sowing and post-harvesting of crops. The cumulative net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (Reco) were −923.04, 3316.65, and 2433.18 g C m−2 over the sugarcane growing season while the values were −192.30, 621.47, and 488.34 g C m−2 over the wheat growing season. The sesbania (green manure) appeared to be a carbon source once it is incorporated into soil. The response of day-time NEE to photosynthetically active radiation (PAR) under two vapor pressure deficit (VPD) sections (0–20 h Pa and 20–40 h Pa) seems more effective over sugarcane (R2 = 0.41–0.61) as compared to the wheat crop (R2 = 0.25–0.40). A decrease in net CO2 uptake was observed under higher VPD conditions. Similarly, night-time NEE was exponentially related to temperature at different soil moisture conditions and showed higher response to optimum soil moisture conditions for sugarcane (R2 = 0.87, 0.33 ≤ SWC < 0.42 m3 m−3) and wheat (R2 = 0.75, 0.31 ≤ SWC < 0.37 m3 m−3) crop seasons. The response of daily averaged NEE to environmental variables through path analysis indicates that PAR was the dominant predictor with the direct path coefficient of −0.65 and −0.74 over sugarcane and wheat growing season, respectively. Satellite-based GPP products from Moderate Resolution Imaging Spectroradiometer (GPPMOD) and Vegetation Photosynthetic model (GPPVPM) were also compared with the GPP obtained from EC (GPPEC) technique. The seasonal dynamics of GPPEC and GPPVPM agreed well with each other. This study covers the broad aspects ranging from micro-meteorology to remote sensing over C4-C3 cropping system [ABSTRACT FROM AUTHOR]

The current study was planned to identify the epidemiological factors associated with leptospirosis in South Gujarat region using neighborhood controls.~Background~Background~A total of 100 cases of leptospirosis occurred in South Gujarat region during the year 2012 were selected using simple random sampling. Three neighbors of the selected cases formed the controls (n = 300). A pretested structured questionnaire was used for data collection and data were analyzed using Epi Info 2007.~Methods~Methods~There was significant association of illiteracy (odds ratio [OR] =1.82, 95% confidence interval [CI] =1.14-2.89), working in waterlogged fields during the reference season (OR = 4.6, 95% CI = 1.6-17.9), swimming/bathing in canals, open air defecation practices, storage of cow dung in or surrounding house, residence in the house made up of cow dung walls, households with access of food to rodents, injuries over hands/foot during the endemic season (OR = 3, 95% CI = 1.8-4.8), and history of skin disease during the endemic season (OR = 4.2, 95% CI = 2-8.5), with leptospirosis. Only 10% of individuals had gumboots for protection. A total of 83 (83%) cases and 240 (80%) controls had taken oral doxycycline chemoprophylaxis (P > 0.05). Cases had taken chemoprophylaxis for a median 4 weeks (range: 1-8) while controls had taken the same for median 8 weeks (range = 1-8) (P < 0.002).~Results~Results~Although the commonly established factors appear to be associated with leptospirosis, the role of host factors seems to play a more important role in determining susceptibility to leptospirosis in exposed individuals.~Conclusions~Conclusions [ABSTRACT FROM AUTHOR]

Objective:This study aims to understand the demographics, mode of trauma, hospital stay, complications, neurological improvement, mortality and expenditure incurred by Indian patients with spinal trauma and ankylosing spondylitis (AS).Methods:Retrospective analysis of the patient data admitted to a tertiary referral hospital from 2008 to 2013 with the diagnosis of AS and spinal trauma was carried out. The variables studied were demographics, mode of trauma, neurological status, neurological improvement, involved vertebral level, duration of hospital stay, comorbid factors, expenditure and complications during the stay.Results:Forty-six patients with diagnosis of AS with spine trauma were admitted over the last 5 years with a total of 52 fractures. All were male patients; 58.6% had injury because of trivial trauma and 78.2% patients presented with neurological injury. C5 C6, C6 C7, C7 D1 and D12 were the most common injured level. Fractures through intervertebral disc were most common in cervical spine. Of the patients, 52.7% had shown neurological improvement of at least grade 1(AIS). Mean expenditure of patient admitted with spinal cord injury (SCI) with AS is 7957 USD (United States dollar), which is around five times the per capita income in India (as per year 2013).Conclusion:Males with AS are much more prone to spinal fractures than females and its incidence may be higher than previously reported. Domestic falls are the most common mechanism of spinal trauma in this population. High velocity injuries are associated with complete SCI. The study reinforces the need for development of subsidized spinal care services for SCI management. [ABSTRACT FROM AUTHOR]

Net photosynthesis rate (PN) has an important role in controlling the ecosystem primary production and it is important to understand the relationships between PN and other physiological and environmental variables to improve and develop models for predicting plant growth and productivity. Here we study utility of ground-based hyperspectral measurements in estimating net photosynthesis during the growth cycle of rice. Ground-based hyperspectral reflectance and micrometeorological data (PN, chlorophyll concentration index (CCI), photosynthetically active radiation (PAR)) were collected from a rice field on three dates in the kharif season at Meerut, Uttar Pradesh, India. Green chlorophyll index (CIg) showed a positive and statistically significant relationship with CCI (R2 = 0.64). Use of single index showed poor fit with PN. The model based on a combination of two variables (vegetation index (VI) × PAR) however proved better in explaining the variance of PN. Among the VI-based combinations comprising three variables which depend on the Monteith light-use efficiency (LUE) concept, models consisting of either CIg or CCI as one variable appeared to be the best in predicting PN with R2 of nearly 0.6. The study concludes that a single index is insufficient to account for factors controlling variation in PN estimation whereas Monteith logic-based combinations have shown its potential for PN estimation by incorporating the chlorophyll based and other vegetation indices as the proxy of LUE and fAPAR (fraction of absorbed photosynthetically active radiation) respectively. [ABSTRACT FROM AUTHOR]

In the present study, the Carnegie-Ames-Stanford Approach (CASA), a terrestrial biosphere model, has been used to investigate spatiotemporal pattern of net primary productivity (NPP) during 2003 over the Indian subcontinent. The model drivers at 2-min spatial resolution were derived from National Oceanic and Atmospheric Administration advanced very high resolution radiometer normalized difference vegetation index, weather inputs, and soil and land cover maps. The annual NPP was estimated to be 1.57 Pg C (at the rate of 544 g C m), of which 56% contributed by croplands (with 53% of geographic area of the country (GAC)), 18.5% by broadleaf deciduous forest (15% of GAC), 10% by broadleaf evergreen forest (5% of GAC), and 8% by mixed shrub and grassland (19% of GAC). There is very good agreement between the modeled NPP and ground-based cropland NPP estimates over the western India ( R = 0.54; p = 0.05). The comparison of CASA-based annual NPP estimates with the similar products from other operational algorithms such as C-fix and Moderate Resolution Imaging Spectroradiometer (MODIS) indicate that high agreement exists between the CASA and MODIS products over all land covers of the country, while agreement between CASA and C-Fix products is relatively low over the region dominated by agriculture and grassland, and the agreement is very low over the forest land. Sensitivity analysis suggest that the difference could be due to inclusion of variable light use efficiency (LUE) across different land cover types and environment stress scalars as downregulator of NPP in the present CASA model study. Sensitivity analysis further shows that the CASA model can overestimate the NPP by 50% of the national budget in absence of downregulators and underestimate the NPP by 27% of the national budget by the use of constant LUE (0.39 gC MJ) across different vegetation cover types. [ABSTRACT FROM AUTHOR]

Argyreia speciosa (Linn. f.) Sweet is a popular Indian medicinal plant, which has long been used in traditional Ayurvedic Indian medicine for various diseases. This plant is pharmacologically studied for nootropic, aphrodisiac, immunomodulatory, hepatoprotective, antioxidant, antiinflammatory, antihyperglycemic, antidiarrheal, antimicrobial, antiviral, nematicidal, antiulcer, anticonvulsant, analgesic and central nervous depressant activities. A wide range of phytochemical constituents have been isolated from this plant. A comprehensive account of the morphology, phytochemical constituents and pharmacological activities reported are included in view of the many recent findings of importance on this plant. [ABSTRACT FROM AUTHOR]

Leaves of Actinodaphne hookeri Meissn (Family Lauraceae; local name: Pisa) has been in use traditionally for the treatment of diabetes and disorders of the urinary tract which are more common in Chattisgarh and eastern part of India. In the present study, leaves of A. hookeri were subjected to phytochemical investigation and evaluated for anti-diabetic activity. The ethanol and the chloroform extract were found to have significant (p<0.01) blood glucose lowering effect. The extracts also significantly (p<0.01) lowered the increased serum cholesterol and low density lipoprotein levels. Preliminary phytochemical investigation revealed the presence of alkaloids, flavonoids, triterpenoids and glycosides as the major constituents in the ethanol extract. The chloroform extract also showed significant (p<0.01) antihyperglycemic activity and contained alkaloids and triterpenes. It is concluded that the antidiabetic activity of A. hookeri may be due to the presence of alkaloids and triterpenes, and might be promising for the development of phytomedicine for diabetes mellitus along with its associated complica…. [ABSTRACT FROM AUTHOR]

The stem bark of Ficus bengalensis is reported to have antimicrobial activity, we planned our work as to evaluate and compare antimicrobial activity of alcoholic extracts of stem bark of F. bengalensis by cup and plate method collected from three different geographical regions viz. Delhi, Gujarat and Uttaranchal. The activity of all the extracts at the dose level 1000 μg/ml, 500 μg/ml and 100 μg/ml were found to be quiet different and all the extracts showed that activity is varied as geography and environmental conditions are changed. [ABSTRACT FROM AUTHOR]