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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 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]

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]

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]